Learning to Detect Compressed Facial Animation Forgery Data with Contrastive Learning

The facial forgery generation, which could be utilised to modify facial attributes, is the critical threat to digital society. The recent Deep Nerual Network based forgery generation methods, called Deepfake, can generate high quality results that are hard to be distinguished by human eyes. Various detection methods and datasets are proposed for detecting such data. However, recent research less considers facial animation, which is also important in the forgery attack side. It tries to animate face images with actions provided by driving videos. Our experiments show that the existed datasets are not sufficient to develop reliable detection methods for animation data. As a response, we propose a facial animation dataset, called DeepFake MNIST+. It includes 10,000 facial animation videos in 10 different actions. We also provide a baseline detection method and the comprehensive analysis of the method and dataset. Meanwhile, we notice that the data compression process could affect the detection performance. Thus creating a forgery detection model that can handle the data compressed with unknown levels is critical. To enhance the performance for such models, we consider the weak and strong compressed data as two views of the original data and they should have similar relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, the algorithm measures the pair-wise similarity within data as the relations and forcing the relations of weak and strong compressed data close to each other, thus improving the performance for detecting strong compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video level contrastive learning for weak compressed data. The experiment results show the proposed algorithm could adapt to multiple compression levels well