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.
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
• Recompression &
• 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
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.
22. The arms race of disinformation…
Google Duplex scheduling a hair salon appointment:
• 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!
Get in touch!
Symeon Papadopoulos email@example.com / @sympap