ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable AI-generated video detection is therefore essential for multimedia forensics, yet remains challenging due to the need to capture spatial artifacts, temporal dynamics, and generalize to evolving generative models. In this paper, we explore reconstruction error as a discriminative forensic cue for AI-generated video detection. By reconstructing input videos with a pretrained WF-VAE, we observe that real and generated videos exhibit distinguishable frame-wise reconstruction error patterns, suggesting that reconstruction errors can reveal thei...
| Authors | Xiaojing Chen, Xinyu Lu, Changtao Miao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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