# Video Face Manipulation Detection Through Ensemble of CNNs

**Authors:** Nicolò Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro  
**arXiv:** [2004.07676](https://arxiv.org/abs/2004.07676)  
**Date:** 2020-04-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2004.07676v1/

## Key points


## Summary

Facial manipulation techniques in videos, such as FaceSwap and deepfake, have become increasingly accessible to the general public in recent years. While these tools can be useful in various fields, they also pose a significant threat if used maliciously. For instance, fake news spreading and cyberbullying through fake revenge porn are some of the negative impacts that can result from their misuse. Detecting whether a face has been manipulated in a video sequence is therefore crucial. In this paper titled "Video Face Manipulation Detection Through Ensemble of CNNs," authors Nicolò Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, and Stefano Tubaro propose a solution to this problem by studying the ensembling of different trained Convolutional Neural Network (CNN) models. The proposed solution involves obtaining different models starting from a base network (EfficientNetB4) using two concepts: attention layers and siamese training. The authors demonstrate that combining these networks leads to promising results for detecting face manipulation in video sequences on two publicly available datasets containing over 119000 videos. This research highlights the importance of developing effective methods for detecting facial manipulations in videos to prevent their harmful effects on society.