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. read more

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Results from the Paper

 Ranked #1 on DeepFake Detection on DFDC (LogLoss metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
DeepFake Detection DFDC EfficientNetB4 + EfficientNetB4ST + B4Att LogLoss 0.4640 # 1
DeepFake Detection FaceForensics++ EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttST AUC 0.9444 # 1
DeepFake Detection FaceForensics++ EfficientNetB4 + EfficientNetB4ST + B4AttST LogLoss 0.3269 # 1