1. Biometric
steganography
By –
Manoj Kumar Mohanty
Department of Computer
Science and Engineering
NIT Rourkela

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Outline
 Introduction
 Application Scenarios
 LSB Insertion method
 Amplitude modulation-based hiding
 Conclusion

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Introduction
 Biometrics data provides uniqueness but
do not provide secrecy.
 For wide spread utilization of biometric
techniques, security of biometric data is
essential.

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Biometric data security
Steganography
Cryptography
BIOMETRICS
DATA
Watermarking

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Application Scenario
 Biometric data, such as fingerprint
minutiae is hidden in a cover image and is
transmitted.
 The function of the host or cover image is
only to carry the data and it need not be
related to the data in any way.

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Cover Images
Synthetic Face Arbitrary
Fingerprint image image
image

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Synthetic Fingerprint

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Cover
Image
Minutiae Watermark Secret
Data Encoder Key
Stego Image
Communication Channel
Watermark Secret
Decoder Key
Extracted Minutiae Data
Steganography-based minutiae hiding

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LSB Insertion Method
 Take the binary representation of the
biometric data and replace it over the
least significant bit(LSB) of each byte
within the cover image.
 In 24 bit color image, the amount of
change will be minimal and difficult to
detect.

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LSB Insertion cont.
 Consider three adjacent pixels (9 bytes)
with the RGB encoding as follows
11110101 11001101 10101001
10100110 11001111 11001010
10101111 00010011 11001000
 Suppose the data to be hidden in binary
is (101101101).

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LSB Insertion Cont.
11110101 11001100 10101001
10100111 11001110 11001011
10101111 00010011 11001000
 9 bits successfully hidden by only
changing 4 bits.

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LSB Insertion Method:
Advantages
 If message bit is same as the pixel’s least
significant bit then no change is required
for that pixel value.
 If pixel value is different from message bit
then effective change in pixel value is still
invisible to human eye.

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LSB Insertion Method:
Disadvantages
 Message can be easily removed by an
intruder as message is in the least
significant bit.
 Further intruder can modify the least
significant bit of all the image pixels.
 The least significant bit may get corrupted
by hardware imperfections or noise.

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Amplitude-modulation based
hiding technique
 Convert the minutiae data into a bit
stream.
 Every field of individual minutiae is
converted to a 9-bit binary
representation.
 A random number generator initialized
with the secret key generates locations of
the host image pixels to be watermarked.

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 Every watermark bit with the value s is
embedded in multiple locations to ensure
better decoding rate of the embedded
information.
 Along with the binary minutiae data, two
reference bits, 0 and 1 are also
embedded to the image.
 These help in calculating an adaptive
threshold in determining the minutiae bit
values during decoding.

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Decoding

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 From decoded watermark bits, the
minutiae data hidden in the host image is
extracted.
 This data hiding model is robust and can
handle attacks such as image cropping,
and JPEG compression.

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Conclusion
 The ability of biometrics-based personal
identification techniques to efficiently
differentiate between an authorized person and
an impostor is one of the main reasons for their
popularity in contrast to the traditional security
techniques.
 However, the security and integrity of the
biometric data itself are important issues.
Application of steganography is a possible
techniques to secure biometric data. Currently
research is going on to increase the data hiding
capacity of the host images and methods for
combining watermarking schemes to achieve
better result.

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References
[1] A.K. Jain, U. Uludag: “Hiding Fingerprint Minutiae into Images.”, in
Proc. AutoID02, NY, March 2002
[2] Chander Kant, Rajender Nath, Sheetal Chaudhary:
“Biometrics Security using Steganography”, in CSC online Journal
“International Journal of Security” Malashiya vol. 2 Issue-1,PP 1-5. 2008.
[3] Adrian Kapczynski, Arkadiusz Banasik: "Biometric Logical Access
Control Enhanced by Use of Steganography Over Secured
Transmission Channel", in IEEE International Conference on Intelligent
Data Acquisition and Advanced Computing Systems: Technology and
Applications, September 2011.
[4] http://biolab.csr.unibo.it/
Biometric System Laboratory, University of Bologna, Italy

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