Verifying Multimedia Content on the Internet
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Summary of problems and research results on the problem of verifying multimedia content on the Internet. Includes results from the REVEAL and InVID research projects. Presented at the Technology Forum, Thessaloniki, May 16, 2018.
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Verifying Multimedia Content on the Internet
- 1. VERIFYING MULTIMEDIA CONTENT ON THE INTERNET Symeon (Akis) Papadopoulos Senior Research Scientist @ CERTH Technology Forum May 16, 2018 @ Thessaloniki, Greece
- 2. A shark thriving in hurricanes https://www.snopes.com/photos/animals/puertorico.asp
- 7. Machine-assisted verification Web image forensics Tweet verification assistant
- 9. Image forensics Lens Optical filter CFA pattern Real-world scene R G G B Imaging sensor (e.g. CCD) CFA interpolat. In-camera SW processing In-camera JPEG compress. DIGITAL CAMERA Digital image Tampered digital image Out of camera SW processing Piva, A. (2013). An overview on image forensics. ISRN Signal Processing, 2013.
- 10. Example: JPEG Ghosts Farid, H. (2009). Exposing digital forgeries from JPEG ghosts. IEEE transactions on information forensics and security, 4(1), 154-160. • JPEG compression • DCT coefficient quantization • Tampered areas: different quantization coefficients • Recompression & subtraction highlights differences • JPEG Ghosts
- 11. Web image forensics Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2015). Detecting image splicing in the wild (web). In International Conference on Multimedia & Expo Workshops (ICMEW), 2015 (pp. 1-6). IEEE
- 12. Image Verification Assistant http://reveal-mklab.iti.gr/ Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y., Bouwmeester, R., & Spangenberg, J. (2016, April). Web and Social Media Image Forensics for News Professionals. In SMN@ ICWSM.
- 14. Tweet Verification Assistant Classification model Reference Dataset Credibility signals Evaluation
- 15. Reference Dataset • 53 events or hoaxes involving false and/or real imagery and videos • 257 cases of “fake” content, 261 of “real” • 10,634 tweets sharing “fake” content, 7,223 tweets sharing “real” content • Examples events: • Hurricane Sandy • Boston Marathon bombing • Sochi Olympics • MA Flight 370 • Nepal Earthquake… https://github.com/MKLab-ITI/image-verification-corpus
- 16. Credibility signals (aka features)
- 17. Building the classification model
- 18. Evaluation 92.5% accuracy in identifying misleading posts 88-98% accuracy depending on language (major languages tested: en, fr, es, nl) Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L., Papadopoulou, O., & Kompatsiaris, Y. (2018). Detection and visualization of misleading content on Twitter. International Journal of Multimedia Information Retrieval, 7(1), 71-86.
- 20. Disinformation in the years to come
- 22. The arms race of disinformation… Google Duplex scheduling a hair salon appointment:
- 23. Summary • Media-based disinformation is complex! • Misleading content tends to be shared more! • There are several technologies available for tackling the problem, each with its limitations • Continuous improvement of AI-based methods is expected to create an arms race of disinformation! • Technology on its own is not sufficient: we need to take into account the human and social facets of the problem: media literacy!
- 24. Thank you for your attention! https://revealproject.eu/ http://www.invid-project.eu/ Get in touch! Symeon Papadopoulos papadop@iti.gr / @sympap