Fingerprint Biometrics vulnerabilities

1 Biometric Template Security

BIOMETRIC TEMPLATE SECURITY

University of Glamorgan: | Farhan Liaqat

University of Glamorgan


Prifysgol Morgannwg

Faculty of Advanced Technology

STATEMENT OF ORIGINALITY

This is to certify that, except where specific reference is made, the work described in
this project is the result of the investigation carried out by the student, and that neither
this project nor any part of it has been presented, or is currently being submitted in
candidature for any award other than in part for the M.Sc. award, Faculty of Advanced
Technology from the University of Glamorgan.

Signed...........………………………………………………………...

(Student)
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Table of Contents
ABSTRACT .......................................................................................................................................................... 6
CHAPTER 1.......................................................................................................................................................... 7
INTRODUCTION .................................................................................................................................................. 7
1. Introduction .......................................................................................................................................... 8
Summary ....................................................................................................................................................... 9
CHAPTER 2........................................................................................................................................................ 10
INTRODUCTION TO BIOMETRICS SYSTEM THREATS AND VULNERABILITIES ............................................... 10
2.1 History of Biometrics Systems ....................................................................................................... 12
2.2 Biometrics Traits ............................................................................................................................... 13
2.2.1 Requirements for Biometrics Traits .................................................................................................... 13
2.2.3 Comparison of Biometrics Trait and Technology ............................................................................. 16
2.3 Biometrics User Authentication ....................................................................................................... 17
2.4 A Standard Biometric System ........................................................................................................ 18
2.5 Threats to Finger Print Biometric System .................................................................................... 21
2.6 Threat Vectors ................................................................................................................................ 21
2.7 Types of Attacks ............................................................................................................................. 22
2.7.1 Physical Attacks ............................................................................................................................. 22
2.7.2 Computer Based Attacks ................................................................................................................ 23
2.7.3 Template Attacks ............................................................................................................................ 24
Summary ..................................................................................................................................................... 25
CHAPTER 3........................................................................................................................................................ 26
PREVIOUS WORK AND LIMITATIONS .............................................................................................................. 26
3 Different Approaches ......................................................................................................................... 27
Summary ..................................................................................................................................................... 28
CHAPTER 4........................................................................................................................................................ 29
FINGERPRINT SENSOR AND IMAGE ................................................................................................................. 29
4.1 Biometric Scanners ........................................................................................................................ 30
4.1.1 Optical Sensors .............................................................................................................................. 31
4.2 Fingerprint Image.......................................................................................................................... 32
4.2.1 Resolution ...................................................................................................................................... 32
4.2.2 Area ................................................................................................................................................ 32
4.2.3 Number of Pixels ........................................................................................................................... 32
4.2.4 Dynamic Range (or depth)............................................................................................................. 33
4.2.5 Geometric Accuracy ....................................................................................................................... 33
4.2.6 Image Quality ................................................................................................................................. 33
4.3 Fingerprint Structure..................................................................................................................... 33
4.4 Fingerprint image Security............................................................................................................ 34
Summary ..................................................................................................................................................... 34
CHAPTER 5........................................................................................................................................................ 36
DESIGN AND IMPLEMENTATION ...................................................................................................................... 36
5. Device and Software............................................................................................................................ 37
5.1.1 Computer ............................................................................................................................................ 37
5.1.2 Fingerprint Reader ............................................................................................................................ 38
5.1.3 Software Development Kit (SDK) ...................................................................................................... 38
5.2. Griaule Software Development Kit (SDK)........................................................................................... 38
5.3. Steganography...................................................................................................................................... 39
5.3.1. What is Steganography Used for? .................................................................................................... 39
5.3.2. Steganography and Biometric Fingerprint Image ........................................................................... 40
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5.4. Steganography Using .Net Algorithms and Techniques ..................................................................... 40
5.5. Generation of Steganography in .Net .................................................................................................. 40
5.6. Fingerprint Image and Steganography ............................................................................................... 41
5.6.2 Application Structure ......................................................................................................................... 41
5.6.2 Application Process ............................................................................................................................ 41
5.6.2.1 Enrolment Process .......................................................................................................................... 42
5.6.2.2 Conversion of Image ....................................................................................................................... 42
5.6.2.3 Steganography................................................................................................................................. 43
5.6.2.4 Stego Library ................................................................................................................................... 44
5.6.3 Decoding the Image ........................................................................................................................... 45
5.6.4 Development Limitations ................................................................................................................... 46
5.7 Fingerprint and Byte Stream ................................................................................................................ 46
5.7.1 Application structure.......................................................................................................................... 46
5.7.2 Application Process ............................................................................................................................ 47
5.7.2.1 Enrolment Process .......................................................................................................................... 47
5.7.2.2 Random Number Generation ......................................................................................................... 47
5.7.2.3 Verification Process ........................................................................................................................ 48
5.7.2.4 Template Attack and Verification ................................................................................................... 49
5.7.2.5 Securing the Template .................................................................................................................... 50
5.7.3 Application Limitations and Advantages .......................................................................................... 50
Summary ..................................................................................................................................................... 51
CHAPTER 6........................................................................................................................................................ 52
RESULTS AND CONCLUSION ............................................................................................................................ 52
APPENDIX A...................................................................................................................................................... 55
APPENDIX B ...................................................................................................................................................... 57
REFERENCES .................................................................................................................................................... 57

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Table of Figures

FIGURE 2 BIOMETRICS DEVICE MARKET 2003 ...................................................................................................... 11
FIGURE 1 FORECAST FOR BIOMETRICS MARKET 2003........................................................................................... 11
FIGURE 3 BRETILLON MEASUREMENT SYSTEM (YORK 2003) ............................................................................... 12
FIGURE 4 BRETILLON FINGERPRINT CARD (FIGURE 4) (YORK 2003) .................................................................... 13
FIGURE 5 DIFFERENT HUMAN TRAITS (FIGURE 5) .................................................................................................... 14
FIGURE 6 TABLE 1 BIOMETRICS TRAIT .................................................................................................................... 16
FIGURE 7 - TABLE 2 TRAITS COMPARISON ............................................................................................................... 16
FIGURE 8 AN EXAMPLE OF BIOMETRIC ATM MACHINE ........................................................................................... 18
FIGURE 9 BIOMETRIC SYSTEM COMPONENTS ........................................................................................................ 18
FIGURE 10 A SAMPLE FINGER PRINT INPUT .......................................................................................................... 19
FIGURE 11 POSSIBLE AREAS OF VULNERABILITIES BASED ON (N.K. RATHA 2001) .............................................. 21
FIGURE 12 OPTICAL SENSOR ................................................................................................................................. 31
FIGURE 13 FINGERPRINT TEMPLATE RESOLUTION ................................................................................................ 32
FIGURE 14 FINGERPRINT RIDGES ........................................................................................................................... 33
FIGURE 15 DELL INSPIRON .................................................................................................................................... 37
FIGURE 16 MICROSOFT FINGERPRINT READERS ................................................................................................... 38
FIGURE 17 ENROLMENT PROCESS ......................................................................................................................... 42
FIGURE 19 IMAGE CONVERSION ............................................................................................................................ 43
FIGURE 20 CREATING STEGO FILE......................................................................................................................... 44
FIGURE 21 DECODING THE IMAGE ......................................................................................................................... 45
FIGURE 23 RANDOM NUMBER ............................................................................................................................... 48
FIGURE 24 VERIFICATION PROCESS ....................................................................................................................... 49
FIGURE 25 ATTACK ............................................................................................................................................... 49
FIGURE 26 SECURING TEMPLATE .......................................................................................................................... 50
FIGURE 27 ALGORITHM ......................................................................................................................................... 56

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Abstract

Technology is becoming an essential part of human life as it increases the attention towards
security and privacy. A person logs into several systems in a day and every log, authenticates
or identifies him into the system. Biometrics provides a reliable and natural solution to verify
a user or to identify a person. The confidence to accept biometric will depend on the
guarantee from the designer that the application is robust with low error rates and security.
But as much biometric systems are authentic, the vulnerabilities remain present. This study
particularly aims towards template security, explaining how biometric systems thoroughly
enlighten the various threats and point of attacks, describing the structure of template and
how it is acquired. Leading toward the solution for the template attacks, the solution
suggested in this paper is robust and customizable providing backward compatibility based
on previous studies.

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C hapter 1

Introduction

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1. Introduction

There have been many events in the world, which directed attention towards security and
safety. Most of the attention to security is regarding passengers in airports. However, there is
one more type of threat which is not visible to a normal person. Hackers, who attack a system
use some techniques modify the information and then manipulate the system to compromise
with the security.

The growth of information technology has been explosive. Technology was never
mishandled in order to access other’s personal information, but now we can evidently see the
propagation of misusing technology in order to penetrate in to every human activity.
Computers have helped human being to explore new horizons in many areas of studies like
human genome, artificial intelligence and application which helped in enhancing human life.
From a small sales application to big financial solutions all information is secured on
database servers and can be accessed from anywhere. Computer systems, and their
interconnecting networks, are also prey to vandals, malicious egotists, terrorists, and an array
of individuals, groups, companies, and governments' intent on using them to further their
own ends, with total disregard for the effects on innocent victims. Apart from attacks on
computer networks externally there are methods of destruction which are unintentional.
Computer security can be defined as a state in which a person cannot compromise with a
system or cannot damage a system intentionally and it is free from external threats. The
purpose of information system security is to optimize the performance of an organization
with respect to the risks to which it is exposed. Security is not only important for Operating
Systems and Networks but we have to secure the physical access to the system as well.

This study begins with introduction to biometrics. Biometrics refers to identify a person
based on his physical or behavioural characteristics. Biometrics is adopted today in most of
the organizations from attendance of employees to border clearance. This study goes to the
greater depth from the origin of biometrics, history and modern technologies, explaining how
the physical and behavioural characteristics are categorised and the mechanism of typical
biometrics system in brief. Later, describing the threats on biometric system which is the core
part of this study. No doubt biometric is very strong and authentic to identify or verify a
person but still it is vulnerable. These threats have been explained in Second chapter.

The main emphasise of the study is on fingerprint biometrics system which has been
implemented vastly over the years. This is due to the fact that it is cheap, accurate and easy to
implement as compared to other biometric systems available in market. In order to spread
biometrics it is important to ensure security integrity of the product. Fingerprint is not only
being used in US or Europe It is also being implemented in south Asia and Middle East now.
Once a product is famous in market the vulnerability increases. Vulnerabilities are of
different nature with regards to biometrics.

Biometric threats are also interlinked with computers as well, because at a level the
information is stored on computer based databases. Hacker can attack the database and steal
the template that holds the important information. Hence, the template is the core part of the
biometric system. The third chapter is going to focus more on the work of other authors,
describing what they have implemented so far and will also highlight the limitations and

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weaknesses. This study is based on these hypothetical literature and concepts to secure
biometrics. The fourth chapter will keep main focus on the template, which will explain how
the template is acquired and which sensor is being used in this study. It will also explain the
mechanism of the sensor and how the image is acquired. Finally will cover, what are the
characteristics of a template. This information will help us to understand the weaknesses and
how to overcome the weakness of the computer based biometric vulnerabilities.

After carefully understanding the current biometrics system, and threats, this study provides a
solution based on combination of different technologies and previous research in chapter five.
This solution will provide more security to the biometrics system which is very necessary. As
biometric traits are the features of human being this cannot be replaced or altered.

Summary

This chapter explains about the structure of this paper. It begins explaining the origin and
reason why it is important to work on biometric template. Biometric template which is not
only the soul of the system but it can be used against the system. This study will prevent the
hackers or attackers to replace and modify the template. The solution proposed in this study is
not only efficient and robust but also cheap and easy to implement and provides a backward
compatibility as it is on software level. All topics are explained step by step helping to
understand the biometric system and solution for the threats.

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C hapter 2

Introduction to Biometrics System Threats and
Vulnerabilities

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Currently, information is mainly secured by using password or some memorable information
from the end user. This type of authentication system is not secure because if someone can
retrieve a bit of information out of end user they can access their bank accounts and personal
computers. These weaknesses in standard validation systems can be avoided if we can use
human body for validation.

The word biometrics originated from the Greek language, Bio means life and Metron means
measures. Modern day technology companies are trying to embed biometrics system with in
hardware and gadgets.

Biometrics is being used almost and it has some befits e.g. reduced cost, easy and simple user
for end user, less need for system support and improved security for the business owners.
Now a day it is being used in many organizations and with many devices e.g. ATM’s,
Passport authentication, border controls, ID cards, Computer system user ID authentication,
Physical access control and fraud prevention.

With the passage of time government and organization are looking forward to improve and
implement biometrics systems for better security. Forecast growths in the market of
biometrics systems have showed a huge change since 1999.

$2,500.00

$2,000.00

$1,500.00
Millions of Dollar
$1,000.00

$500.00

$0.00
1999 2000 2001 2002 2003 2004 2005

Figure 1 Forecast for Biometrics Market 2003

There are many biometrics systems available in the market which I am going to discuss later
on but fingerprint scanning systems is amongst the leading ones. In 2001 it was half of the
market was claimed by the fingerprint scanning devices. According to Dan riley, vice
president of SecuGen “One of the main reasons was because fingerprint identification and
verification is a very old, tried-and-tested technology, with lots of confidence in the
technology and the ability to develop excellent-quality, low-cost solutions,” (Biometrics
2001).

Finger Scan

Voice Scan
10%
49%
15% Signature Scan
12%
Iris Scan
1%
6% 3% 4%
Figure 2 Biometrics Device Market 2003

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The reason why finger print biometrics system are being used so widely all over the world is
because it is one of the earliest methods implemented to identify a person. Nevertheless, there
are still some organizations that do not adopt this mechanism as they think it is not very
authentic. Companies are trying to improve and evolve it which we are going to discuss later
on.

As we speak about the cost of biometrics devices fingerprint are once again the cheapest ones
which are available in market and can purchase from 60$ to 130$ in market from many
different vendors. Comparatively, iris scan is four to six time expensive than fingerprint
scanners. According to British National Physical Laboratory facial scan has become third
largest amount revenue in world. (Biometrics 2001)

2.1 History of Biometrics Systems

Biometrics has been previously related to forensics science. Modern day biometrics system is
more related to forensics than security purpose. According to CSI survey 15% out of 687
organizations are using biometrics system.

Early references to biometrics, as a method to identify a person were around thousand year
back. East Asian potters use to place their finger print on products as a brand identity. In
ancient Egypt trusted traders were identified based on certain characteristics such as height,
eye colour and complexion. (JD.JR., Biometrics Background 2000)

Biometrics was not very famous as field in late 18th Century when to police clerks from Paris
found a solution that taking measurement of different body parts of adult can identify the
convicted criminals as the body parts of adult don’t change overtime and can be used to
identify later on. (Record 2002)

The Bretillon system, also known as bretillonage and anthropometry has been widely
accepted. It is used around the world for decades depict a series of Bretillon measurements as
they were used in USA at the beginning of 20th century. The measurements included the
width and length of the head and of the right ear, the breadth of the outstretched arms, the
length of the left foot, the left form arm and the left little finger as well as the body and trunk
heights. (Canton 2203)

Figure 3 Bretillon Measurement System (York 2003)

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An abrupt end to the use of anthropometrics was caused by an incident in 1903, when two
identical twins, that in later investigation were discovered to be separated at birth, were
registered at the united state penitentiary at Leavenworth, Kansas with measurement as close
enough to identify as one person. They looked exactly the same so the identification was only
possible only using fingerprints. (Canton 2203)

Figure 4 Bretillon Fingerprint Card (Figure 4) (York 2003)

In 1891 the inspector general of Bengal police, Sir Edward Henry, got interested in the work
of Sir Francis Galton and others considering fingerprints as a mean of identification. In 1896
an order was issued by Henry, which in addition to Bretillon finger prints should be taken
from every prisoner. With the help of his assistant he was able to make classification system
allowing thousand of fingerprints to be easily filled, searched and traced. Henry was assigned
as Assistant Commissioner of Scotland Yard in 1901 where the first finger print bureau was
established in the same year. After the failure of anthropometry in 1903, the Henry
fingerprint system quickly gained worldwide acceptance as the means of identifying
criminals. It is still used in much the same way today (Record 2002).

Automated means of human recognition first appeared as an application for physical access
in the early 1970s. One of the first commercially available biometrics system was a finger
measurement device called identimat, which was installed n 1972 to serve a wall street
company, Shearson Hamil, as a time keeping and monitoring application. (JD.JR., N.M and
P.T, Biometrics Identity Assurance in The Information Age 2003)

2.2 Biometrics Traits

There have been many human characteristics used to identify human for biometrics application. To
categorize human characteristics some question come in mind, what are the requirements? Are there
any general identifiers? What are the technologies can they meet the general requirements? This
section is going to cover the answers to these questions.

2.2.1 Requirements for Biometrics Traits

There are some general requirements which should meet to qualify with a Biometric system.

• Universality: Every Human Has.
• Uniqueness : This Means That Trait Should Be Different From Person to Person
• Permanente : The Trait Should Not Change With Time
• Collectability: The Trait Can Be Measured
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According to (A.K., S and S 1999) there are some more factors which should be considered for
categorizing traits.
• Performance: To achieve the best possible identification environmental factors should be
consider with the combination of minimum cost.
• Acceptability: Future user should accept the system.
• Circumvention Resistance: It should be difficult to fool with the system.
• Cost Effectiveness: Maintenance and installation should be in reasonable cost.

We cannot find all the characteristics or requirements in a single biometrics device but each
system or device has its own strength and qualities.

2.2.2 Classification of Biometrics Traits

According to the National Institute of Standards (2003) Biometrics system is divided into two
categories of biological measurements.

• Physiological Characteristics
• Behavioral Characteristics

Figure 5 Different Human Traits (Figure 5)
i. Physiological Characteristics

These traits are obtained from the human anatomy e.g. DNA, Fingerprint, and Face, Iris or the
retina. Data is generated by the analysis and the measurement of structure of the human body
parts.

It is important to understand that physiological traits are not necessarily genetically determined;
therefore, a differentiation between genotype and phenotype features must be made. (Daugman
1999)

• Genotype

There are about 1% people in world, that have similar genetic code or in other words we
can say they are monozygotic twins. An example which we have discussed of west
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brothers, in genetics monozygotic twins share all their characteristics like blood group,
DNA structure and gender etc.

• Phenotype

These are the features which are unique unlike to genotypic features. In the west brothers
for example finger prints were use to identify them. Fingerprints and iris are one of the
examples of phenotypic characteristics.

Some features can expose both genotype and phenotype factors of a human like face which
changes throughout the age, but still identical twins can look similar in any stage of age.

ii. Behavioral Characteristic

Today if we want to open a bank account in the UK, they require our signatures on a device and
later on if you want to make a query regarding your account they match your signature with the
stored information on the computer. Human has some behaviors which are unique from person to
person. According to International Biometrics Group “Behavioural characteristics are based on
an action taken by a person. (Group 2003) Behavioural biometrics, in turn, is based on
measurements and data derived from an action, and indirectly measure characteristics of the
human body. Voice recognition, keystroke-scan, and signature-scan are leading behavioural
biometric technologies. One of the defining characteristics of a behavioural biometric is the
incorporation of time as a metric – the measured behaviour has a beginning, middle and
end.” (Group 2003)

Humans, learn their behaviour or are trained hence it can be changed. By the passage of time
with the growth of age prominent changes also occur in the behaviour of human so it
becomes more difficult to achieve them. (JD.JR., N.M and P.T, Biometrics Identity
Assurance in The Information Age 2003) Still behavioural characteristics can be used as
biometrics traits even if they are not permanent. Below in the given table you can see the
categorization of biometrics traits in groups. There are some traits which are not used widely
in the table e.g. Blood Chemistry and body odour. But we are going to study commonly used
traits in detail.

Category Biometrics Trait
Hands Fingerprints
Palm Prints
Hand Geometry
Hand, Palm and Wrist Vein Patterns
Spectroscopy Skin Analysis
Nail bed Scanning
Head and Face
Face Recognition
Iris
Retina
Ear Shape and Size
Other Physical Characters
Body Salinity
Blood Chemistry
Body Odor
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DNA
3D Thermal Imaging
Neural Wave Analysis
Behavioral Characteristics
Gait Pattern
Voice Recognition
Signature Recognition
Keystroke Dynamics
Figure 6 Table 1 Biometrics Trait

2.2.3 Comparison of Biometrics Trait and Technology

To get a better understanding of why some technologies are more preffered and are being used
widely in market, we have to create a table based on analysis and perception of (A.K, R and S,
BIOMETRIC- Personal Identification in Network Society 1999) and (Corporation 2002).

Perform

effective
Accepta
Perman
Univers

resistan
Circum
vention
Unique

Collect
ability

Cost-
bility
ance
ence
ality

ness

ness
Characteristics

Finger Print Med Hi Hi Med Hi Med Med Med

Hand Geo. Med Med Med Hi Med Med Med Med

Retina Hi Hi Med Low Hi Low Hi Low

Iris Hi Hi Hi Med Hi Low Hi Low

Face Hi Low Med Hi Low Hi Low Med

Vascular Pat. Med Med Med Med Med Med Hi Med

DNA Hi Hi Hi Low Hi Low Low Low

Ear Shape Med Med Hi Med Med Hi Med ?

Body Odor Hi Hi Hi Low Low Med Low ?

Facial Thermo. Hi Hi Low Hi Med Hi Hi Med

Voice Med Low Low Med Low Hi Low Hi

Signature Low Low Low Hi Low Hi Low Med

Keystroke Low Low Low Med Low Med Med Hi

Gait Pattern Med Low Low Hi Low Hi Med ?

Figure 7 - Table 2 Traits Comparison
In the table we can see that the comparison is based on available technologies based on available
basic eight requirements. They have been compared using “Hi”, “Med” and “Low”. Question
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mark indicates that the data is not available. Cost effectiveness of biometrics system has not been
calculated yet of some technologies.
From the above chart we can conclude many results as explained below.
• Behavioral biometrics performance is not as good as we compare it to physiological.
• Permanent traits are DNA, Iris, Retina Body odor and Fingerprint.
• DNA and Facial Thermograph shows better performance in the chart, Body Odor shows
that it is unique permanent and universal. Iris and DNA can make a very strong biometric.
But some technologies still need improvement like Body Odor.
• Biometrics system like DNA and Iris are expensive comparatively Fingerprint and Hand
Geometry are cheaper.
• Acceptability is higher when information or data is gathered without the information of
end user e.g. Facial Thermograph and ear shape recognition. User mostly likes to provide
identity which they are familiar with like voice recognition and signature dynamics.

2.3 Biometrics User Authentication

In early days to identify a person some sort of physical information used to be stored. This
information was in several formats e.g. Picture, Physical measurements, Fingerprint or a picture.
Modern days same methods are used in a different way, these information are kept into a database
and then cross matched to verify a person.

But sometimes due to injuries or accident we cannot authorize a person. In one case a person had
his burnt his finger accidentally hence the prints were damaged so when he tried to scan his finger
from the device it was not allowing him to do so.

People, have the tendency to leave their information where ever they go e.g. latent finger prints on
surfaces, recorded voice print and video recording of face can generate bogus authentications.
Secondly a trained attacker can intercept the information stored in the database and replace them
with the fake one. Therefore, accurate information is only possible if the system can ensure that
the information stored in the system is of the live people. (JD.JR., N.M and P.T, Biometrics
Identity Assurance in The Information Age 2003)

Even though biometric technologies are far from being an authentication panacea, they represent a
very promising method, especially when combined with other authentication techniques. (A.K, R
and S, BIOMETRIC- Personal Identification in Network Society 1999)

Again, it has been demonstrated that every system created by human is defeated by human. In
terms of authentication techniques, all factors suffer from fundamental weaknesses. (JD.JR., N.M
and P.T, Biometrics Identity Assurance in The Information Age 2003)

Every authentication system can be cracked e.g. Information like password and pins can be
hacked. Properties like cards can be stolen and biometric information can be swapped by
someone.

Some systems accept two types of authentication token based a knowledge based. For instance,
when we need to make a transaction from the ATM, we have to swipe in the card then enter the
pin. In 1999 25% people write down their pins on the card and due to these companies had to face
hug loss. (Anil K. Jain 1999)

Now suppose we replace the pin with biometrics authentication. Let’s take Iris scan, as a personal
identifier some companies already tried to use it as a replacement of PINs.
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Figure 8 An Example of Biometric ATM Machine
There might be some complications like position problem of user but if it is implements it will be
far stronger then PINs.

2.4 A Standard Biometric System

Apart from the technologies, whether it is an iris, finger print or DNA all biometric devices
follow almost similar mechanism I m going to explain it in detail below. A biometrics system
is based on five basic subsystem according to (Jhon D. 2003) and (J.L. Wayman n.d.) For i.e.
acquisition, transmission, signal processing, data storage and decision policy.

Data Signal Decision Policy
Matching
Review

Pattern
Biometric
matching Match
?
Quality Score

Presentation Quality
Control
Accept
?

Sensor Extraction

Sample Sample Template Yes/No

Transmission Data Storage

Compression Templates
Expansion
Sample
` Transmission Images
Channel

Based on (John D. Woodward 2003; J.L. Wayman August 2002)

Figure 9 Biometric System Components

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i. Data Acquisition

(James Wayman 2004) States that biometric data flow begins with the collection of
physiological and behavioural characteristics and every biometric system is based on two
assumptions.

• Uniqueness: Biometric trait is distinctive among all human beings.
• Repeatability: Measurements can be repeated over time

Figure 10 A Sample Finger Print Input
A sensor is used to measure characteristic of an individual. For each system biometric system
is standardize so if information is collected from one system can be matched on other systems
as well. The information captured by the sensor is stored into database as a template. Every
template has its own attributes depending on what type of trait is being used or read by the
sensor.

ii. Transmission

The captured template is stored in a standard format e.g. image acquired by the sensor is
saved as JPEG (Join Photographic Expert Group) facial images, WSQ (Wavelet/Scalar
Quantization) for fingerprint and CELP (Code Excited Linear Predication) is used for voice
data. This information is then transmitted to data processing so it can be saved in the
database. Sometimes the sensor is located somewhere else and data processing is somewhere
else. During the transmission of the data compression is done to save the bandwidth. Due to
compression the quality can be poor. Developments in technologies are introducing new
methods of compression so loss can be reduced.

iii. Signal Processing

As described in Figure 10, signal processing is performed in three steps, initially it is a
mechanism in which the template is created from the information that is received from the
sensor.

• Feature Extraction
• Quality Control
• Pattern Matching

iv. Feature Extraction
It is a mechanism in which the biometrics system extracts the required information out of the
trait from a particular biometric device. In this scenario, it is an iris scanner which willbe
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observe how the feature extraction works with it. This task is performed by localizing the iris,
pupil and both eyelid boundaries, excluding pupil and eyelashes from the photo and creating
an iris mapping that are invariant to size, distance, magnification and pupil dilation. After that
an iris code is generated(Daugman 1999) we will discuss it later.

v. Quality Control

After the feature extraction a quality check is performed which calculates the score output. If
the received signal from the device is insufficient and there is some incomplete information.
For e.g. If there is some dust on the sensor or some metal is on the sensor, automatically a
request is sent back to the user for rescan. There have been many major updates in quality
checking in biometrics system in past few years.

vi. Pattern Matching

After the extraction and quality check pattern matching is performed, if there is a mismatch
with the data, the enrolments takes place. This is the process in which new user enrols
himself and the information is stored in the data base along some external information passed
by the system owner or administrator.

There are two types of enrolments further in one case if user claims about an identity then the
match is 1:1 otherwise system has to perform a 1: N match. In which the pattern is matched
with all the available templates in database. As a result of matching the decision policy
system checks the score which is a measurement of similarity between the database templates
and the one extracted from the device.

vii. Data Storage

After signal processing these templates are stored to a database management system so when
a user enrol system can make a comparison, Databases for biometrics systems varies from
systems to systems depending on the nature of application.

For systems which are based on 1:1 matching. Templates are stored on something which can
be in possession of an individual e.g. magnetic strip cards or smart cards. When someone
tries to identify them the system asks for a token and then verifies the image with the
template on the card. The database is used in such cases as well.

In 1: N matching systems a centralized database is designed. These kinds of systems perform
better and also the occurrence of faults and errors can be vastly reduced. These databases are
divided then into smaller partitions. In this way the templates are matched with corresponding
information in the database instead of whole database.

viii. Decision Policy

This subsystem determines the results of the match whether they are right or wrong. These
results are based on quality score and matching score received from the signal process. For
some systems, it can be very simple but for alternatives it can be sophisticated e.g. a simple
system might have a matching score and if a signal generates the highest score it is matched.

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In a sophisticated system there can be many factors i.e. time variant threshold, user dependant
and high score.

2.5 Threats to Finger Print Biometric System

When a hacker attacks a typical system it is difficult from a biometric security system. In
Denial of Service Attack and attacker corrupts the authentication so the users cannot use it.
Hacker bombards so many bogus access requests on biometric system, an online
authentication server that processes access request to a point where the server’s resources
cannot handle any more queries. In circumvention, an attacker gains access of the system by
destroying the authentication application. This threat can lead us to the modification of data
or access to the information which is not allowed to access by external users. (Maltoni 2005)

In contamination attacker copies the biometric information of a user e.g. a fingerprint from
the surface and use that print to access biometric security system or access the information. In
repudiation attacker denies that he accessed the system and can argue that False Accept Rate
phenomenon associated with biometric system might caused the problem. In collusion
legitimate user with wide privilege to the system is that attacker (System Administrator)
(Maltoni 2005).

2.6 Threat Vectors

Understanding how biometrics is categorized based upon the physical properties. Similarly
biometrics attacks are performed on the system at different levels, some of these attacks are
on physical level and with the personal contact with biometric system e.g. bogus biometric
attack is a type of physical attack in which attacker uses latent fingerprint and use it on the
system to compromise with security. After compromising the security it can manipulate the
system steal personal information of a person and let access to unauthorized people to a
certain area. This section will explain how many types of attacks can be performed on which
stage during a biometric process which has been explained above in detail.

We have discussed some types of attack above; according to (N.K. Ratha 2001) there are
about eight types of attacks which can be performed on a typical biometric system. These
possible attacks areas are called threat vectors.

1 Sensor `

2
7 6
3 Feature Extraction

4

5 Matcher Template Database

8

Decision

Figure 11 Possible Areas of Vulnerabilities Based on (N.K. Ratha 2001)

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Computer systems have been the target of attacks from a variety of sources almost since they
were first used. Early examples of exploitation were generally related to fraud. In more recent
times, hackers, organised crime and a variety of other cyber-criminals have attacked
computer systems. Information systems also have to deal with viruses, worms and Trojans
seeking to disrupt systems or steal data. Again, this is not unique to biometric systems and
there are now well-established standards, frameworks, policies and process as well as
legislative support, for the protection of information systems. The most important factors are
proper systems and security design and proper implementation and on-going management,
rather than the use of biometrics per se. (Roberts November 2005)

The first threat to biometrics technology was recognized by several authors (D, et al. 2003)
(A.K., S and S 1999) (G.L and F 2003). When an authentication is used on large scale, the
reference database has to be made available to many different verifiers, who in general,
cannot be trusted. Especially in a network environment, attacks on database pose a serious
threat. It was shown explicitly by Matsumoto et al (G.L. and F 2003). that using information
stolen from database, artificial biometrics can be constructed to impersonate people.
Construction of artificial biometrics is possible if only a part of the template is available. Hill
(A, A.K and J 2003) showed that if only a minute template of a fingerprint is available, it is
possible to successfully construct artificial biometrics that pass authentication.

The second threat was addressed by Schneier (S and A.K 2002). The problem is concisely
paraphrased by: “Theft of biometrics is theft of identity.”

The threat is caused by the fact that biometrics contains sensitive personal information. It is
shown by the author (A.K, R and S, BIOMETRIC- Personal Identification in Network
Society 1999) (T and F n.d.) (X and L 2003) That a fingerprint contains certain genetic
information.

2.7 Types of Attacks

Schneier (B 1999) compares traditional security systems with biometric systems. The lack of
secrecy (e.g. leaving fingerprint impression on the surface we touch), and non replace ability
(e.g., once the biometric data is compromised, there is no way to return to a secure situation,
unlike replacing a key or password) are identified as the main problems of biometric systems.

(D, et al. 2003) Describe the typical threats, for genetic authentication application, which may
result in quite different effects for traditional and biometrics-based systems. In Denial of
Service (DoS), an attacker corrupts the authentication system so that legitimate users cannot
use it, for a biometric authentication server that processes access request (via retrieving
template from a database and performing matching with the transferred biometric data).
Biometrics attacks have been categorized in three sections according to their nature as below.

2.7.1 Physical Attacks

These attacks are mainly on the biometric devices sensor or biometric readers. Most of these
attacks have been performed on fingerprint biometric system.

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i. False Enrolment

The accurate data of legitimate user is enrolled, if it is fake then data will be accurate but it
will be matched incorrectly. For example a passport application once registered the system
data will identify it and give privileges to the system

ii. Bogus Physical Biometrics

We have numerously seen in the movies, when someone tries to access a security area
breaking a biometric system. Person uses a fingerprint left from some surface. This vector is
most prominent one from all. This attack is performed without any technical knowledge it is
very cheap and easy in modern days when we have digital cameras. These attacks are made
only on iris, palm and fingerprint biometrics systems.

• Bogus Digital Biometrics

When we talk about biometrics attacks, masquerade attacks are on the top of list. They
are fake digital patterns which are used to break biometrics systems. Second ones are
reference attacks in which attacker gathers technical information of a biometrics system
and has digital copies of the templates to replace them from the database or during the
enrolment.
• Latent Print Reactivation

Human sweats glands produce oil which sweats from hands. When someone touches
surface marks of print are left on it. These prints can be copied and used on biometrics
devices. These types of attacks are done on finger and palm print reader.

2.7.2 Computer Based Attacks

In this type of attack mainly the target is computer system i.e. server, databases or networks
connected with the system.

i. Override Feature Extraction

In this type of attack hackers interfere with the feature extraction process, this attack is also
used to disable a system or for DoS. It is usually conducted on hardware or software
firmware.

ii. System Parameters

In such kind of attacks system parameters are changed. If someone changes the percentage or
score of FAR (False Acceptance Rate) that will result that poor quality data can be verified.

iii. Match override

In these types of attacks, matching decisions are changed or ignored. Parameters are changed
by authorised person only or the hacker should have access to the system.

iv. Decision Override

This is also called a bypass attack which ignores all the process. In this type of attack the
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decision is changed data is injected the decision. In this type of attack some physical
tempering may be involve.

v. Modification of Rights

If someone gets unauthorised access to system administration accounts and creates a user
with admin privileges. This can cause a DoS attack.

vi. Systems Interconnections

If two systems are interconnected it is possible to get two types of threats, one is from the
external system which is interconnected with biometrics system and second one is the
network which is connecting the two systems. Usually these kinds of threats are handling by
the people administrating biometrics systems.

vii. System Weaknesses

Weaknesses and Flaws in the design of a system may create some vulnerability. Some time
organizations use customization and integrate their Biometrics security system with the
secondary system. These weaknesses maybe occur in

• Operating Systems i.e. Server or clients
• Storage Management i.e. Operating Systems
• Biometrics Software
• Database
• Sensors
• System Configurations

These problems are noticeable in other technologies as well as biometric systems but we have
to accept these as weaknesses which may lead hacker to compromise with the system.

viii. Denial of Service Attack

DoS are the worst vector threat. They vary in different types of attack from power loss to
system attacks design to corrupt biometrics security systems. Changes in the environmental
condition dust or light can change the quality of biometrics sensor reading. Adding electrical
or radio frequency can corrupt the data e.g. spilling liquid on sensor or introducing portable
light to the sensor. DoS attacks are usually noisy and they can be noticed easily.

2.7.3 Template Attacks

These attacks are mainly on templates and are usually on databases. The nature of these
attacks is modification of template and then attacker compromise with the system.

i. Reuse of Residual

In some biometric systems templates are stored in temporary memory after extraction. If
hacker gains access to the memory, they can copy the information and use it next time.

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ii. Data Injection

This type of attack both the system and stored data are compromised. If attackers gains access
to the system, it would be easier to manipulate data in the database as it is not encrypted. For
these types of attack system and template knowledge is essential.

iii. Template Modification

Templates are stored on different media (Cards, Tokens or Biometrics Devices). In this type
of attack hacker modifies or adds information to the storage media. In this type of scenario
information is added and then unauthorised access is allowed by providing a false ID.

iv. False Data Injection

This type of attack takes places in three steps. The attack can also be placed in the category
of man in middle attack. First the data is intercepted when sensor transfers the information to
processing system. Mostly this is don’t on physical level e.g. data is stored on a card or RFID
and it is unencrypted first. Secondly, the data is modified and then finally the signal is
replayed. Encryption of the data increases the complication of the data and also is used as a
defence strategy.

v. Synthesised Feature Vector

Hill Climbing is a technique which is mentioned in various articles on biometric security.
According to (Anil K. Jain 2005) in this technique false biometrics information is injected
into the system but every time the changes into templates are made which can increase the
matching score. In this technique access to system match score and communication channels
is necessary.(Anil K. Jain 2005)

Templates attack is different from above mentioned two attacks as they can be secured by
several security measures. If a template is copied once system can compromise to some
extent which can grant access to attacker to any level. This paper will mainly focus on
template attacks.

Summary
This chapter explains traits, mechanism of biometric system and threats to biometric systems.
Biometrics is divided based or different properties called biometric traits, which are
categorized under physical and behavioural traits. Mechanism of biometric system has been
explained in depth from the acquisition of biometric trait to storage in database and
verification of a user. By understanding in detail a typical biometric system threats can be
outlined. These threats are further segmented based on their nature.

• Physical
• Computer Based
• Templates Attack

Templates attacks are most dangerous attack in biometric system. As if a template is acquired
and attacker can compromise with the system then nothing can be done on physical and
computer based security.
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C hapter 3

Previous Work and Limitations

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3 Different Approaches

Analysing the above mentioned attacks, an attacker can clandestinely obtain biometric data of
legitimate users e.g. lifting a latent fingerprint and constructing a three-dimensional mould
and use to access system. Further the biometric data associated with specific application can
be used to another unintended application e.g. it can be used to retrieve medical records.
Cross application usage of biometric can be more often as many organizations prefer
biometric applications. (D, et al. 2003)

The problem may arise from the above mentioned attacks on biometrics systems are raising
concerns as more and more biometrics systems are being deployed both commercially and in
government applications. (Enhanced Border Security and Visa Entry Reform 2002) This is
along with the increase in the size of the population using these systems and the expanding
application areas i.e. visa, border control, health care, e-commerce etc. may lead to privacy
and security related breaches.

As I have discussed several types on attacks on biometric system. There are some attacks
mentioned above which are mainly related to biometric templates. The template is the core of
a biometric system. In this paper I am going to propose a system which will reduce the threats
to template modification or bogus attack on a fingerprint biometric system.

Several work has been done on biometric template security, but not been implemented
practically in any biometric technology. In order to prevent hill climbing attack Southar (C
n.d.) has suggested the use of coarsely quantized match scores by the matcher. However
Adler (A. A May 2004), demonstrated that it is still possible to estimate the unknown
enrolled image although the number of iterations required to converge is significantly higher
now.

Yeung and Pankanti (M and S 1999) describe an invisible fragile watermarking technique to
detect regions in a fingerprint image that has been tampered by the attacker. In the proposed
scheme the chaotic mixing procedure is employed to transform visually perceptible
watermark to a random-looking textured image in order to make it resilient against attacks.
This mixed image is then embedded in fingerprint image. The author shows that the presence
of the watermark does not affect the feature extraction process. The use of watermark also
imparts copyright capability to identifying the origin of the raw fingerprint image.

IBM is one of the leading vendors in biometrics industry. Many of IBM products have built
in fingerprint sensors i.e. laptops. IBM suggested that if the techniques presented here for
transforming biometric signals differ from simple compression using signal or image
processing techniques. While compression of the signal causes it to lose some of its spatial
domain characteristics, it strives to preserve the overall geometry. (N.K., J.H. and R.M. 2001)
That is, two points in a biometric signal before compression are likely to remain at
comparable distance when decompressed. This is usually not the case with our distortion
transforms. Our technique also differs from encryption. The purpose of encryption is to allow
a legitimate party to regenerate the original signal. In contrast, distortion transforms
permanently obscure the signal in a noninvertible manner (N.K., J.H. and R.M. 2001).

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Ferri (L, et al. 2002) proposed an algorithm to embed dynamic signature features into face
image present on ID cards. These features are transformed into a binary stream after
compression (used in order to decrease the amount of payload data). A computer generated
hologram converts this stream into the data that is finally embedded into blue channel of the
image. During verification the signature features hidden in the face image are recovered and
compared against the signature obtained on-line, Ferri (L, et al. 2002) report that any
modification of the face image can be detected, thereby disallowing the use of fake ID cards.

On the other hand Jain and Uludag suggest the use of steganography principles to hid
biometric data in host image. This is particularly useful in distributed systems where raw
biometric data may have to be transmitted over a non secure communication channel.
Embedding biometric data in an innocuous host image prevents an eavesdropper from
accessing sensitive template information. The author also discusses novel application where
in the facial features of a user are embedded in a host fingerprint image. In this scenario, the
watermarked fingerprint image of a person may be stored in a smart card issued to that person
at an access control site. The fingerprint of the person possessing the card will first be
compared with the fingerprint present in the smart card. The eight coefficients hidden in the
fingerprint image can then be used to reconstruct the user face thereby serving as a second
source of authentication (A.K and U, Hiding Biometric Data 2003).

Pros and Cons

In summary, their published work attempts to deal with the biometric template security issue.
Some of them address how to handle biometric based key schemes. The most promising
approaches tolerate the variations in biometric solutions, but few of them are practically
feasible for biometric template as the rate of matching biometric template decrease with the
variations.

This paper will work on the purposed solution provided by Jain and Uludag mentioned.
Steganography can be used to hide encryption inside the template. Steganography will be
discussed in detail in chapter five. This paper will introduce an application which will use
steganography with fingerprint biometric template on software template. This is easy and
robust also it can be used with previous hardware.

Summary
Security has been concern since long time and people have been working on it. Similarly
goes with biometrics. Authors directed our attentions to different threats and provided
possible solutions over the years. Some of the solutions were implemented practically but
results were not desired. Improvements have been made in such areas specifically talking
about fingerprint biometrics watermarking and steganography helped a lot in encryption of
biometrics.

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C hapter 4

Fingerprint Sensor and Image

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Modern day organizations are developing their own solutions for business purpose. These
businesses are running on internet and millions of users are logging into the website
purchasing products and spending money over the internet through credit cards. There is no
proper authentication system available for end user over the web apart for traditional security
asking for memorable question or security pin etc. In this section I am going to explain and
design a solution for modern business, which can be implemented easily and integrated with
any software and hardware of fingerprint biometric system, also providing more
authentication and security to the product.

Indeed, a growing number of financial services firms’ are strongly considering the use of
biometrics technology, sooner rather than later, because of heightened security concerns
sparked by the Sept. 11 terrorist attacks and skyrocketing fraud rates. Biometric identification
systems use individuals' unique physical or behavioural characteristics, such as fingerprints
or voice patterns, to identify them. (Mearian n.d.)

According to Meridien Research Inc. in Newton, Mass., consumer fears and losses due to
fraud are a strong enough incentive for institutions to invest large sums of money in
biometrics. And with 500,000 cases of identity theft in the U.S. each year, consumers are
ready to accept biometrics at the cost of increased privacy and more intrusive methods of
identification, according to a recent report by Meridien. (Mearian n.d.)

Many software vendor organizations are providing solutions for e business to protect identity
theft. These solutions are software based totally and any fingerprint hardware can integrate
with them. These software integrations are quite simple and flexible. Companies can use
biometrics system in any department and for any purpose. Similarly this biometric software
can be use over the internet. Suppose a customer needs to get online and purchase a product
from a web site. At the time of payment when the verification is required customer is using a
biometric verification by using fingerprint scanner, instead of providing information related
to its bank account. This can prevent the attacker from getting information of the user and
reduce the risk to identity theft. This type of solution is not expensive as now a day’s many
hardware vendors are providing built in fingerprint sensors.

The question which arise here is that how much secure is this type of solution over internet,
considering the above mentioned attacks on a biometric system in chapter two. An attacker
can perform a DOS attack on the system or decision override. Also can inject new template
into the system and make changes to the template information inside database. First of all the
main threat is to be point out. As mentioned above mostly attacks are done on templates and
five types of template attacks are available.

4.1 Biometric Scanners

Before continuing further, a question arises that what is this fingerprint template which has
been stated so many times. Most of the personal recognition systems do not store fingerprint
image itself but store only numeric data after extracting the feature from the image.
Sometimes it may be important to save the acquired image into the database.

The first fingerprint scanner was introduced about thirty years back. Before that ink technique
was used this is still being used by law and enforcement agencies. AFIS has created a
database over the years which contains both fingerprint images acquired offline and live scan
scanners. (D, et al. 2003)
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The offline fingerprint is usually taken by spreading black ink on the finger and then the
impression is taken on a paper. This impression is later on converted into digital format with
the resolution of 500 dpi. (D, et al. 2003)

For live scan fingerprint scanners are used. Most important part of the scanner is sensor.
There are three types of fingerprint sensors are available in the market. Optical solid state and
ultrasound (D, et al. 2003) in this paper optical sensor will be discussed only.

4.1.1 Optical Sensors

In this paper more emphasis will be on optical sensor as it will be used further. A simple
optical sensor is based on three components

1. Prism
2. Light
3. CCD or CMOS

Figure 12 Optical Sensor
This is the oldest and most live fingerprint scanning technique used today. The finger touches
the top side of the glass prism, but when the ridges touch the surface the valleys remains on a
certain distance as shown in the image. Light is illuminated from the left side from light
emitting diodes. The light is then reflected randomly from the prism and focused through a
lens on CCD or CMOS. (D, et al. 2003)

When the finger is very dry, it does not make a uniform contact with the sensor surface. To
improve the formation of fingerprints from dry fingers, whose ridges do not contain sweat
particles, some scanner producers use silicon coating, which favours the contact of the skin
with the prism. With the aim of reducing the cost of optical devices plastic is nowadays often
used instead of glass for prism and lenses, and CMOS cameras are mounted instead of more
expensive CCDs. (D, et al. 2003)

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4.2 Fingerprint Image

After the impression is taken from the sensor, it is then converted into image file which is in
most of the cases is in .Jpeg format. There are some parameters for the characterisation of
fingerprint image which is as following.

4.2.1 Resolution

This indicates the number of dots or pixels per inch (dpi). 500 dpi is the minimum resolution
standard for FBI-complaint scanners and is met by many commercial devices. 250 to 300 dpi
is probably the minimum resolution that allows the extraction algorithms to locate the
minutiae in fingerprint patterns. Minutiae play a primary role in fingerprint matching, since
most of the algorithms rely on the coincidence of minutiae to declare whether the two
fingerprint impressions are of the same finger. (D, et al. 2003)

Figure 13 Fingerprint Template Resolution
In Figure 13, there are samples of same fingerprint image in different resolutions. It is clear
that decreasing the resolution size of image can affect the matching algorithm.

4.2.2 Area

The size of rectangular area sensed by a fingerprint scanner is a fundamental parameter. The
larger the area is the more ridges and valleys are captured and more distinctive the fingerprint
becomes. An area greater than or equal to (1 X 1) as per FBI standards permits a full plain
fingerprint impression. Recently companies are reducing the area to reduce cost and to have a
smaller device size. (D, et al. 2003)

4.2.3 Number of Pixels

The numbers of pixels can be simply derived by the resolution and the area. A scanner
working with r dpi over an area can be expressed by. (D, et al. 2003)

Height (h) × width (w) inch2 = rh × rw pixels

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4.2.4 Dynamic Range (or depth)

This denotes the numbers of bits used to encode the intensity value of each pixel. Colour
information is not useful for fingerprint recognition and therefore almost all the available
fingerprint scanners acquire greyscale images. The FBI standard for pixel bit depth is 8 bits,
which yields 256 levels of gray. Actually, some sensors capture only 2 or 3 bits of real
fingerprint information and successively stretch the dynamic range to 8 bits in software. (D,
et al. 2003)

4.2.5 Geometric Accuracy

This is usually specified by the maximum geometric distortion introduced by the acquisition
device, and expressed as a percentage with respect to x and y directions. Most of the optical
fingerprint scanners introduce geometric distortion which, if not compensated, alters the
fingerprint pattern depending on the relative position of the finger on the sensor surface. (D,
et al. 2003)

4.2.6 Image Quality

It is not easy to precisely define the quality of a fingerprint image, and it is even more
difficult to decouple the fingerprint image quality from the intrinsic finger quality or status.
In fact when the ridge prominence is very low, for example a manual workers and elderly
people, when the fingers are too moist or to dry, when they are incorrectly presented to the
sensor. Most of the scanners produce a poor quality image. (D, et al. 2003)

4.3 Fingerprint Structure

A fingerprint usually appears as a series of dark lines that represent the high, peaking portion of the
friction ridge skin, while the valley between these ridges appears as white space capacitive and are
the low, shallow portion of the friction ridge skin. Fingerprint identification is based primarily on
the minutiae, or the location and direction of the Ridge endings and bifurcations (splits) along a
ridge path. (http://cte1401-01.sp00.fsu.edu/holly.html n.d.)

Figure 14 Fingerprint Ridges
The image presents an example of fingerprint features. The types of information that can be
collected from a fingerprint's friction ridge impression include the flow of the friction ridges, the
presence or absence of features along the individual friction ridge paths and their sequence, and
the intricate detail of a single ridge. Recognition is usually based on the first and second levels of
detail or just the latter.

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4.4 Fingerprint image Security

As it has been mentioned above, some of the some techniques were suggested by several
authors in chapter 2. These solutions have not been implemented yet on any biometrics
system or to some extent they have been implemented but not available in market. This study
will provide a basic understanding of the structure and mechanism of fingerprint biometric
and template, which will lead us toward the solution for securing the template. The idea is to
use steganography with in biometric template to hide encrypted information to verify along
with the biometric template. In this way if an attacker attacks a and manipulate the biometric
template it will not compromise with the system. The reason will be the template used to
attack the system lacks the encrypted information which is stored in database.

Summary
It is necessary to understand the system before suggesting a solution. This chapter focuses on
how fingerprints are acquired and what are its components and how can we secure it. Adding
steganography in template is a challenge as it can affect matching algorithm. With the
knowledge of template structure it can be clear how we can embed a key inside the image
without disturbing the template features. Also it will help to decide whether changes can be
made on hardware level.

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C hapter 5

Design and Implementation

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As mentioned above the aim of this study is to design an application which can increase the
security in fingerprint biometric systems i.e. security of biometric template. This hypothesis
can be achieved by creating a small module which can embed encrypted information into the
template and then decode it at the time of verification. The encrypted key will be stored in the
database separately for verification purpose. If the attacker replaces the template it can reduce
the risk that template will compromise as lack of the computer generated encrypted key.

To prove the hypothesis two applications are developed on different technologies. One
application is on Microsoft VB .Net and Microsoft Access. The second application is on
Visual C# and Microsoft SQL Server. The concept is same but both work on different
approach which is explained in detail below.

5. Device and Software

The required Devices and Software is as following:

• Computer for application development running Microsoft windows operating system
• A biometric fingerprint reader with optical sensor.
• Biometric software development kit (SDK) compatible with windows and fingerprint
reader.

The specifications of these devices are as following.

5.1.1 Computer

The computer which will be used in this study is a laptop machine specifications are as
following.

Name Dell
Model Inspiron 6400
Processor Speed 1.86 GHz Intel T2130 Genuine

Figure 15 Dell Inspiron

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5.1.2 Fingerprint Reader

The Microsoft Fingerprint Reader has a small, efficient design. The device is almost three
inches long, and a little over an inch wide, and a quarter inch high with a weight of slightly
more than an ounce. The reader screen itself is a little over an inch long, and slightly less than
inch wide. A split red/silver circle encompasses the plastic reader screen. The reader itself is
a slightly sticky plastic material. When the keyboard is on, the reader lights up in the same
way the bottom of the optical mouse do.

Figure 16 Microsoft Fingerprint Readers

5.1.3 Software Development Kit (SDK)

The Software Development Kit (SDK) used in this application is from Griaule for visual
basic 2005 .Net.

5.2. Griaule Software Development Kit (SDK)

The SDK which is used in this study is Griaule Fingerprint SDK. It is the most efficient SDK
available in marker at the moment which can be integrated into several languages and works
with many sensors. Some features of SDK are as following.

• Plug and play for Microsoft fingerprint device.
• Easy integration with applications
• Very small template size 1KB approximately
• Image can be stored along with the template
• 1:1 and 1:N matching capabilities
• Microsoft .Net support
• FVC2006 recognised

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FVC compared several SDK and Griaule SDK results were highly accurate and stable in
matching with low error rates. Secondly Griaule provides easy integration with hardware and
language. One feature which Griaule SDK provides is storing image along with the template
in the database. Storing image of the fingerprint can help in embedding information using
steganography.

Before moving further it is important to understand what steganography is and how it can be
used in securing template.

5.3. Steganography

Steganography is really nothing new, as it has been around since the times of ancient Rome.
For example, in ancient Rome and Greece, text was traditionally written on wax that was
poured on top of stone tablets. If the sender of the information wanted to obscure the message
- for purposes of military intelligence, for instance - they would use steganography: the wax
would be scraped off and the message would be inscribed or written directly on the tablet,
wax would then be poured on top of the message, thereby obscuring not just its meaning but
its very existence (Johnson 1995)

According to Dictionary.com, steganography (also known as "steg" or "stego") is "the art of
writing in cipher, or in characters, which are not intelligible except to persons who have the
key; cryptography" (Dictionary.com n.d.). In computer terms, steganography has evolved into
the practice of hiding a message within a larger one in such a way that others cannot discern
the presence or contents of the hidden message (Howe 1993 - 2001). In contemporary terms,
steganography has evolved into a digital strategy of hiding a file in some form of multimedia,
such as an image, an audio file (like a .wav or mp3) or even a video file.

5.3.1. What is Steganography Used for?

Like many security tools, steganography can be used for a variety of reasons, some good,
some not so good. Legitimate purposes can include things like watermarking images for
reasons such as copyright protection. Digital watermarks (also known as fingerprinting,
significant especially in copyrighting material) are similar to steganography in that they are
overlaid in files, which appear to be part of the original file and are thus not easily detectable
by the average person. (Schneier 1996) Steganography can also be used as a way to make a
substitute for a one-way hash value (where you take a variable length input and create a static
length output string to verify that no changes have been made to the original variable length
input) (Schneier 1996). Further, steganography can be used to tag notes to online images (like
post-it notes attached to paper files). Finally, steganography can be used to maintain the
confidentiality of valuable information, to protect the data from possible sabotage, theft, or
unauthorized viewing (Radcliff 2002).

Unfortunately, steganography can also be used for illegitimate reasons. For instance, if
someone was trying to steal data, they could conceal it in another file or files and send it out
in an innocent looking email or file transfer. Furthermore, a person with a hobby of saving
pornography, or worse, to their hard drive, may choose to hide the evidence through the use
of steganography. And, as was pointed out in the concern for terroristic purposes, it can be
used as a means of covert communication. Of course, this can be both a legitimate and an
illegitimate application. (Westphal 2003)
Page | 39


5.3.2. Steganography and Biometric Fingerprint Image

Understanding the idea of steganography, it can be quite useful to secure fingerprint image in
the database from attacker. Let’s suppose,

5.4. Steganography Using .Net Algorithms and Techniques

There are three different techniques you can use to hide information in a cover file:

• Injection (or insertion)

Using this technique, you store the data you want to hide in sections of a file that are ignored
by the processing application. By doing this you avoid modifying those file bits that are
relevant to an end-user—leaving the cover file perfectly usable. For example, you can add
additional harmless bytes in an executable or binary file. Because those bytes don't affect the
process, the end-user may not even realize that the file contains additional hidden
information. However, using an insertion technique changes file size according to the amount
of data hidden and therefore, if the file looks unusually large, it may arouse suspicion. (Weiss
nd)

• Substitution

Using this approach, you replace the least significant bits of information that determine the
meaningful content of the original file with new data in a way that causes the least amount of
distortion. The main advantage of that technique is that the cover file size does not change
after the execution of the algorithm. On the other hand, the approach has at least two
drawbacks. First, the resulting stego file may be adversely affected by quality degradation—
and that may arouse suspicion. Second, substitution limits the amount of data that you can
hide to the number of insignificant bits in the file. (Brainos nd)

5.5. Generation of Steganography in .Net

In the substitution techniques, a very popular methodology is the LSB (Least Significant Bit)
algorithm, which replaces the least significant bit in some bytes of the cover file to hide a
sequence of bytes containing the hidden data. That's usually an effective technique in cases
where the LSB substitution doesn't cause significant quality degradation, such as in 24-bit
bitmaps.

For example, to hide the letter "a" (ASCII code 97 that is 01100001) inside eight bytes of a
cover, you can set the LSB of each byte like this:

10010010
01010011
10011011
11010010
10001010
Page | 40


00000010
01110010
00101011

The application decoding the cover reads the eight Least Significant Bits of those bytes to re-
create the hidden byte—that is 0110001—the letter "a." As you may realize, using this
technique let you hide a byte every eight bytes of the cover. Note that there's a fifty percent
chance that the bit you're replacing is the same as its replacement, in other words, half the
time, the bit doesn't change, which helps to minimize quality degradation.

5.6. Fingerprint Image and Steganography

5.6.2 Application Structure

Classes

Classes used in this application are as below

• InputBox.cs
• DBClass.cs
• Util.cs

These classes are provided with fingerprint SDK samples and provide method to acquire
image from sensor and extract features.

References

• AxGrFingerXLib
• GrFingerXLib
• Stdole
• System
• System.Data
• System.Drawing
• System.Windows.Form
• System.XML
• stego

5.6.2 Application Process

Application will mainly start from enrolment process of the finger. User will place the finger
on sensor and image will be acquired in application from the sensor. After the acquisition of
the image SDK normally extracts the features of the image which is called template and
stores the template in the database. To achieve the goal this method is modified.

Page | 41


5.6.2.1 Enrolment Process

Enrolment process takes place when user place finger on the sensor and image is acquired by
the application into the image box. Once the enrolment process takes place image format is
converted which is explained further.

Encrypted

Text

Template Image with key

Database

Figure 17 Enrolment Process

Figure 18 Enrolment Process

5.6.2.2 Conversion of Image

After the image is acquired it is converted from 8 bit format to 24 bit due to the stego
requirements from the library.
Bitmap bm8bit = new Bitmap(sfdImage.FileName);

Bitmap bm24bit = new Bitmap(bm8bit.Width, bm8bit.Height,
System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Page | 42


Graphics g = Graphics.FromImage(bm24bit);

After the image is converted into 24 bit format text are embedded using steganography
techniques.

Figure 19 Image Conversion

5.6.2.3 Steganography

Once the image is ready and in 24 bit format cover file is created which will be explained in
next section. Message and password is assigned to the file and after that the file is created
using encode button as shown in figure.

Page | 43


Figure 20 Creating Stego File

5.6.2.4 Stego Library

This library is developed by Giuseppe Naccarato and Alessandro Lacava. Provides a simple
API to encode an image and decode it using simple method. There are two interfaces to
perform this task

IcoverFilel: This method requires three parameter stego file name message to hide and
password. This method hides the message inside the stego file.

If the code in project is over the method mention above can be seen in these lines and explain
the usage.

ICoverFile cover = new BMPCoverFile(pic);

// Create the stego file
cover.CreateStegoFile(stegoFile, message, password);

Page | 44


Result("Message hidden successfully");

Image stegoPic = new Bitmap(stegoFile);
FitPic(stegoPic, picStegoFileEnc);
picStegoFileEnc.Image = new Bitmap(stegoPic);
stegoPic.Dispose();

IStegoFile: This method extract hidden message from the file. This method has been used in
project on following lines this opens the stego file and displays the hidden message into the
text box as shown in image below.

// Open the stego file
IStegoFile stego = new BMPStegoFile(stegoFile, password);

// Show the hidden message
txtMessageDec.Text = stego.HiddenMessage;

5.6.3 Decoding the Image

Image decoding is reverse of steganography process as mention above in section stego library how it
is performed in the application. Password and the file path are provided in the option box. After click
on the decode button it shows the hidden value in the text box.

Figure 21 Decoding the Image

Page | 45


5.6.4 Development Limitations
• Image Size

First issue during the development was to change the image resolution. Microsoft
Fingerprint reader produces an image of 256 colours. For steganography the method used
in this application the requirement of image was of 24 bit. For this purpose the small
module was written to convert the image from 256 colours to 24 bit.

• Image Storage

Next challenge in this application was the storage of image in the access database. Access
has some limitations in data types. Image features extracted into template can be stored
into database using OLE Object data type. Due to this it was difficult to store image in
access as compare to SQL server which will be explained further later on.

• Verification Process
In verification process user will place finger on the sensor. Image will be acquired in
application. Now at this stage multiple verifications will take place. As there are some
limitations which are explained.

5.7 Fingerprint and Byte Stream

This application is designed using Microsoft Visual C# and Microsoft SQL server 2005.
Griaule SDK is again used in the same way with the small modification of DB Class.

5.7.1 Application structure
Classes

These are the main classes used in the application

• InputBox.cs
• DBClass.cs
• Util.cs

These classes are provided with SDK by Griaule. Which provide default method to add
information in database and to manipulate the features of the image in the image box; these
classes also provide flexibility for programming end.

References

• AxGrFingerXLib
• GrFingerXLib
• Stdole
• System
• System.Data
• System.Drawing
Page | 46

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