Sequential DeepFake detection is an emerging task that aims to predict the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employing conventional Transformer architectures for detection. However, these methods lack dedicated design and consequently result in limited performance. In this paper, we propose a novel Texture-aware and Shape-guided Transformer to enhance detection performance. Our method features four major improvements. Firstly, we describe a texture-aware branch that effectively captures subtle manipulation traces with the Diversiform Pixel Difference Attention module. Then we introduce a Bidirectional Interaction Cross-attention module that seeks deep correlations among spatial and sequential features, enabling effective modeling of complex manipulation traces. To further enhance the cross-attention, we describe a Shape-guided Gaussian mapping strategy, providing initial priors of the manipulation shape. Finally, observing that the latter manipulation in a sequence may influence traces left in the earlier one, we intriguingly invert the prediction order from forward to backward, leading to notable gains as expected. Extensive experimental results demonstrate that our method outperforms others by a large margin, highlighting the superiority of our method.

这篇论文介绍了一种新颖的纹理感知和形状引导的Transformer模型，用于增强顺序DeepFake检测性能，包括纹理感知分支、双向交互交叉关注模块、形状引导高斯映射策略，并且通过将预测顺序由前向后反转来提高性能。实验证明该方法在性能上明显优于其他方法。

面纹感知与形状引导的顺序DeepFake检测Transformer