Authors
Yingjie Liu, Zonghui Guo, Ximeng Li, Jie Zhang, Haiyong Zheng, Shiguang Shan
Published in
IEEE transactions on pattern analysis and machine intelligence. Volume PP. Oct 06, 2026. Epub Oct 06, 2026.
Abstract
Face Forgery Video Detection is a critical yet challenging task in determining whether a digital facial video is authentic or forged. Existing methods often rely on isolated spatial or coarsely fused spatio-temporal features, which are insufficient to mine spatio-temporal forgery cues and lead to unsatisfactory generalization performance. To this end, we strive to unravel general spatio-temporal forgery cues across three progressive levels: (1) spatial regional suspicions, (2) temporal momentary anomalies and gradual inconsistencies, and (3) spatio-temporal cumulative distortions. Accordingly, we design a hierarchical cooperation module to identify regional suspicion cues by coordinating intra-frame features across different Transformer layers of Image Encoder. Then, we devise a gradual future guidance module to unravel temporal cues by iteratively aggregating historical anomaly cues and gradually propagating inconsistency cues into future frames. Finally, we introduce a historical review module that unravels distortion cues via momentum accumulation and spatio-temporal review from future to historical frames. These three modules form Spatio-Temporal Forgery Cue Unraveling (STFCU) framework, sequentially highlighting spatial discriminative features by unraveling spatio-temporal forgery cues bidirectionally between historical and future frames. Extensive experiments and ablation studies demonstrate effectiveness of STFCU method, achieving state-of-the-art performance across diverse unseen datasets and manipulation methods.
PMID:
42837239
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 18
- Comments 0