V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors
As generated videos become increasingly realistic, reliable video forgery detection is increasingly important. Existing studies typically optimize and use video forgery detectors as black boxes, while the latent forgery-discriminative knowledge inside them remains largely unexplored. Instead of continuing to rely on resource-intensive full-model retraining to steadily improve detection performance, we ask whether video forgery detection can also be achieved by uncovering and activating sparse forensic knowledge within the detector. We find that forgery-discriminative knowledge is not uniformly distributed across the full representation space, but is concentrated in a sparse set of functio...
| Authors | Shichao Kan, Chengpeng Hong, Jingtong Dou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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