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.

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

4. http://www.gizmodo.co.uk/2015/03/that-viral-photo-of-
todays-solar-eclipse-totally-not-real/

6. https://revealproject.eu/ http://www.invid-project.eu/

7. Machine-assisted verification
Web image
forensics
Tweet verification
assistant

8. Web image forensics

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.

13. Tweet verification assistant

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.

19. User interface
http://tiny.cc/tw_verify

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