Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection
The visual quality of AI-generated videos has improved drastically in recent years, making it increasingly difficult for humans to distinguish between real and synthetic media. In this work, we evaluate the robustness and applicability of four state-of-the-art motion-based AI-generated video detectors. We identify significant preprocessing and sampling biases in three of the four methods and demonstrate that they account for a substantial portion of their reported performance. Furthermore, we find that these detectors are highly sensitive to motion patterns specific to their evaluation datasets, where AI-generated videos generally exhibit less inter-frame movement than real videos. We sho...
| Authors | Joren Michels, Lode Jorissen, Nick Michiels |
|---|---|
| Primary category | cs.CV |
| Published | 1 July 2026 |
| Last updated at source | 6 October 2026 |
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