  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.
