  With the continuous research on Deepfake forensics, recent studies have
attempted to provide the fine-grained localization of forgeries, in addition to
the coarse classification at the video-level. However, the detection and
localization performance of existing Deepfake forensic methods still have
plenty of room for further improvement. In this work, we propose a
Spatial-Temporal Deepfake Detection and Localization (ST-DDL) network that
simultaneously explores spatial and temporal features for detecting and
localizing forged regions. Specifically, we design a new Anchor-Mesh Motion
(AMM) algorithm to extract temporal (motion) features by modeling the precise
geometric movements of the facial micro-expression. Compared with traditional
motion extraction methods (e.g., optical flow) designed to simulate
large-moving objects, our proposed AMM could better capture the
small-displacement facial features. The temporal features and the spatial
features are then fused in a Fusion Attention (FA) module based on a
Transformer architecture for the eventual Deepfake forensic tasks. The
superiority of our ST-DDL network is verified by experimental comparisons with
several state-of-the-art competitors, in terms of both video- and pixel-level
detection and localization performance. Furthermore, to impel the future
development of Deepfake forensics, we build a public forgery dataset consisting
of 6000 videos, with many new features such as using widely-used commercial
software (e.g., After Effects) for the production, providing online social
networks transmitted versions, and splicing multi-source videos. The source
code and dataset are available at https://github.com/HighwayWu/ST-DDL.
