AI or Real: Detecting Partially Altered Videos Under Resource-Constrained Environments
The proliferation of generative video models has shifted the practical detection threat from fully fabricated clips to partially manipulated footages. Although modern detectors achieve strong accuracy using foundation backbones of 400M+ parameters, their resource footprint precludes edge deployment. In this paper, we present a lightweight full-frame detector for partially manipulated AI-generated video, designed for deployment on edge hardware without face-detection preprocessing. The system distills a DINOv2-Base teacher into a frozen MobileNetV3-Small student through a pipeline that combines temperature-annealed soft-label transfer, attention-diversity regularization, frame-level superv...
| Authors | Tamoghna Chakraborty, Md Nurul Absur, Sourya Saha et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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