V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors
This research offers a new perspective on understanding and exploiting the intrinsic forensic capability of video forgery detectors, potentially reducing the need for resource-intensive full-model retraining for researchers and practitioners in digital forensics.
This paper investigates the internal workings of video forgery detectors, revealing that forgery-discriminative knowledge is concentrated in a sparse set of specialized neurons rather than being uniformly distributed. They propose V-FIND, a framework that localizes critical layers and identifies these latent anchor neurons, forming a compact forensic subspace that achieves strong detection performance with only a lightweight linear classifier trained on a frozen backbone.
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 functionally specialized neurons. Based on this insight, we propose a video forgery-intrinsic neuron discovery (V-FIND) framework. V-FIND first localizes critical layers that exhibit pronounced discrepancies between real and forged videos, and then identifies latent anchor neurons that consistently carry forgery-discriminative signals, organizing them into a compact forensic subspace. With the original backbone frozen and only a lightweight linear classifier trained, this subspace still delivers strong detection performance across multiple external benchmarks for generated videos. Further neuron intervention experiments provide direct evidence for the functional specificity of the discovered neurons. Overall, these results suggest that video forgery detectors contain sparse, extractable, and reusable forgery-discriminative knowledge, offering a new perspective on understanding and exploiting their intrinsic forensic capability.