  Recent advances in face forgery techniques produce nearly visually
untraceable deepfake videos, which could be leveraged with malicious
intentions. As a result, researchers have been devoted to deepfake detection.
Previous studies have identified the importance of local low-level cues and
temporal information in pursuit to generalize well across deepfake methods,
however, they still suffer from robustness problem against post-processings. In
this work, we propose the Local- & Temporal-aware Transformer-based Deepfake
Detection (LTTD) framework, which adopts a local-to-global learning protocol
with a particular focus on the valuable temporal information within local
sequences. Specifically, we propose a Local Sequence Transformer (LST), which
models the temporal consistency on sequences of restricted spatial regions,
where low-level information is hierarchically enhanced with shallow layers of
learned 3D filters. Based on the local temporal embeddings, we then achieve the
final classification in a global contrastive way. Extensive experiments on
popular datasets validate that our approach effectively spots local forgery
cues and achieves state-of-the-art performance.
