  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.

Computer Vision and Pattern Recognition;Multimedia;Image and Video Processing