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Video & AI: capabilities and limitations of AI
in detecting video manipulations
Athens, Nov. 2019
Research Director, CERTH-ITI
Dr. Vasileios Mezaris

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• Deep fakes are new…
• …but fakes are not a new phenomenon!
AI as facilitator of disinformation
Haiti earthquake video hoax (Jan. 2010): Video from a previous
earthquake in California published by major media, slightly modified
(cropped) and re-circulated in social media as a Haiti video; picked-
up and aired by mainstream media for hours
Presidential fake picture (Jan. 2013): Video frame of a surgery
operation in Mexico in 2008, was sold to and was mistakenly
published by a top newspaper, supposedly depicting the
Venezuelan president Hugo Chavez under surgery in a Cuban
hospital.
https://www.youtube.com/watch?v=JbzVhzNaTdI

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• What makes deep fakes different?…
• Easier to create high-quality(?) fakes -> more people can do it
• Easier to create multiple fakes -> more media items can be created in
support of a disinformation campaign; can give rise to Distributed Denial of
Truth (DDoT) attacks
• …and what remains the same?
• A deep fake is usually an altered version of an original video; only the
means of introducing the alteration differs
• The techniques typically applied for detecting video-borne disinformation
are still useful (but there is also room for developing techniques specifically
tailored to detecting deep fakes)
AI as facilitator of disinformation
www.invid-project.eu

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• How can AI help?
• Understand the structure of the video; select characteristic frames for
visual inspection or reverse search on the web
• Find near-duplicate videos, or semantically-related videos (of the same or
similar incidents)
• Video forensics
AI for combating disinformation

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• Tool for verification of selected newsworthy videos
InVID Verification Plugin
• Free! Get it from: https://www.invid-project.eu/verify
Continues to be developed; now with new look in v0.71 (released June 7, 2019)
•
• Check prior video
use: reverse video
search on the Web
• Check contextual
information: Social-
media-based
contextual analysis
• View location, time
and other video
metadata
A browser plugin to debunk fake news
and to verify videos and images
>15.000
users
• Keyframe/image inspection by magnifying glass
• Check image (keyframe) forensics

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• Tool for advanced video verification
• Check prior video use at the video-segment level:
InVID Verification Application
•
•
• Reverse video search also in the InVID repository; inspection by parallel playback
of query and duplicate video
• Check video origin and rights:
• Video logo detection
• Video rights management
• Check video forensics:
• Video forensic filters
• Frame-level video inspection
• Check additional contextual
information
• Store the automatic analysis results and manually add evaluation
comments and notes
• Free demo version accessible at: http://invid.condat.de

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InVID Verification Plugin – Reverse search
https://www.youtube.com/watch?v=OVAxQA3gMEo
“Video, the crowd in panic flees
from Notre Dame in Paris after
the attack of an armed man”

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InVID Verification Plugin – Reverse search
https://www.youtube.com/watch?v=OVAxQA3gMEo
“Video, the crowd in panic flees
from Notre Dame in Paris after
the attack of an armed man”

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InVID Verification Plugin – Reverse search
https://www.youtube.com/watch?v=OVAxQA3gMEo
“Video, the crowd in panic flees
from Notre Dame in Paris after
the attack of an armed man”

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InVID Verification Plugin – Reverse search
https://www.youtube.com/watch?v=OVAxQA3gMEo
“Video, the crowd in panic flees
from Notre Dame in Paris after
the attack of an armed man”

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InVID Verification Plugin – Reverse search
https://www.youtube.com/watch?v=OVAxQA3gMEo
“Video, the crowd in panic flees
from Notre Dame in Paris after
the attack of an armed man”

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• Video forensics – a quick AI-based approach
• Filters generating heat maps (e.g. Cobalt: compares the original video with
a modified version of it, re-quantized using MPEG-4 at a different quality
level, and generates the error video)
• Use deep learning architectures (e.g. GoogLeNet, ResNet) to learn from
both the video itself, and the outputs of these filters
• No knowledge of the generator’s characteristics is assumed
• Results are promising, but not a complete, deployable solution yet [1][2]
AI-based video forensics
[1] M. Zampoglou, F. Markatopoulou, G. Mercier, D. Touska, E. Apostolidis, S. Papadopoulos, R. Cozien,
I. Patras, V. Mezaris, I. Kompatsiaris, "Detecting tampered videos with multimedia forensics and deep
learning", Proc. 25th Int. Conf. on Multimedia Modeling (MMM2019), Thessaloniki, Greece, Springer
LNCS vol. 11295, pp. 374-386, Jan. 2019.
[2] "Video verification in the fake news era", V. Mezaris, L. Nixon, S. Papadopoulos, D. Teyssou (Editors),
Springer, 2019.

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AI-based video forensics
Detected
Fakes
Fakes not
detected
(lower
resolution)
Videos from the InVID Fake Video Corpus
1280x720p480x360p
320x240p 320x240p

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• Video forensics – a steganalysis- & AI-based approach
• Steganalysis features are extracted from each frame [3]
• Sliding windows of 128x128 pixels are considered
• High pass filtering of pixel values, then calculate and quantize residuals; co-
occurrence matrices are calculated
• A recurrent autoencoder is trained on pristine frames of the video
• The trained autoencoder assesses the remaining frames of the video
• Forged spatial regions in manipulated frames are singled out as anomalous
• Good results plus localization of the forgery; but sensitive to encoding
AI-based video forensics
[3] D. D’Avino, D. Cozzolino, G. Poggi, L. Verdoliva, “Autoencoder with recurrent neural networks for
video forgery detection”. In: Electronic Imaging 2017.7 (2017), pp. 92–99.

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• Video forensics – a steganalysis- & AI-based approach
AI-based video forensics
Video from the UNINA dataset
http://www.grip.unina.it/

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• Looking at the greater picture – directions & thoughts on deep
fakes and AI-based video forensics
• Manipulated videos and deep fakes still usually come from an original: if
you find the original out there, you have documented the faking
• Reverse video search is still a powerful tool
• AI has great potential; is already used in different techniques for
combatting disinformation, and will be used even more for video forensics
and detecting deep fakes
• But, no single tech seems to be the solution; we need a portfolio of tools [4]
• Complete automation is a challenge!
AI-based video forensics
[4] "Video verification in the fake news era", V. Mezaris, L. Nixon,
S. Papadopoulos, D. Teyssou (Editors), Springer, 2019.

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Dr. Vasileios Mezaris
Information Technologies Institute
Centre for Research and Technology Hellas
Thermi-Thessaloniki, Greece
Tel: +30 2311 257770
Email: bmezaris@iti.gr, web: http://www.iti.gr/~bmezaris/
For more information on the InVID project, visit www.invid-project.eu
Follow InVID on Twitter @InVID_EU
Contact

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This work was supported in part by the EU’s Horizon 2020
research and innovation programme under grant agreement
H2020-687786 InVID.
Acknowledgement