
In the last few years, several techniques for facial
manipulation in videos have been successfully developed and
made available to the masses (i.e., FaceSwap, deepfake, etc.).
These methods enable anyone to easily edit faces in video
sequences with incredibly realistic results and a very little effort.
Despite the usefulness of these tools in many fields, if used
maliciously, they can have a significantly bad impact on society
(e.g., fake news spreading, cyber bullying through fake revenge
porn). The ability of objectively detecting whether a face has
been manipulated in a video sequence is then a task of utmost
importance.
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation
techniques. In particular, we study the ensembling of different
trained Convolutional Neural Network (CNN) models. In the
proposed solution, different models are obtained starting from a
base network (i.e., EfficientNetB4) making use of two different
concepts: (i) attention layers; (ii) siamese training. We show that
combining these networks leads to promising face manipulation
detection results on two publicly available datasets with more
than 119000 videos.

Video Face Manipulation Detection Through Ensemble of CNNs

In the last few years, several techniques for facial
manipulation in videos have been successfully developed and
made available to the masses (i.e., FaceSwap, deepfake, etc.).
These methods enable anyone to easily edit faces in video
sequences with incredibly realistic results and a very little effort.
Despite the usefulness of these tools in many fields, if used
maliciously, they can have a significantly bad impact on society
(e.g., fake news spreading, cyber bullying through fake revenge
porn). The ability of objectively detecting whether a face has
been manipulated in a video sequence is then a task of utmost
importance.
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation
techniques. In particular, we study the ensembling of different
trained Convolutional Neural Network (CNN) models. In the
proposed solution, different models are obtained starting from a
base network (i.e., EfficientNetB4) making use of two different
concepts: (i) attention layers; (ii) siamese training. We show that
combining these networks leads to promising face manipulation
detection results on two publicly available datasets with more
than 119000 videos.

**Session Chairs**: prof. Xavier Alameda-Pineda, prof. Paolo Rota

OS T5.1: Image  Processing

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
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- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Edoardo Daniele Cannas

2

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Video Face Manipulation Detection Through Ensemble of CNNs

Video Face Manipulation Detection Through Ensemble of CNNs

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