Although the deepfake detection based on convolutional neural network has achieved good results, the detection results show that these detectors show obvious performance degradation when the input images undergo some common transformations (like resizing, blurring), which indicates that the generalization ability of the detector is insufficient. In this paper, we propose a novel block shuffling learning method to solve this problem. Specifically, we divide the images into blocks and then introduce the random shuffling to intra-block and inter-block. Intra-block shuffling increases the robustness of the detector and we also propose an adversarial loss algorithm to overcome the over-fitting problem brought by the noise introduced by shuffling. Moreover, we encourage the detector to focus on finding differences among the local features through inter-block shuffling, and reconstruct the spatial layout of the blocks to model the semantic associations between them. Especially, our method can be easily integrated with various CNN models. Extensive experiments show that our proposed method achieves state-of-the-art performance in forgery face detection, including good generalization ability in the face of common image transformations.

本文提出了一种基于块打乱学习的深度伪造检测方法，通过巧妙引入块内外的随机重排，增强了检测器的鲁棒性及语义连结建模能力，结合CNN模型实现面部伪造检测，在通用性与鲁棒性的表现方面都获得了良好的效果。

区块洗牌学习用于Deepfake检测