Motion-based AI video detectors exploit motion biases in evaluation datasets and drop to near-random performance on rebalanced data, while frequency-based detectors remain robust.
Vidprom: A million-scale real prompt-gallery dataset for text-to- video diffusion models.Advances in Neural Information Processing Systems, 37:65618–65642
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Reference-frame dominance in self-attention suppresses motion in image-to-video models; DyMoS rebalances attention from generated frames to the reference during initial denoising steps to improve dynamics while preserving fidelity.
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Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection
Motion-based AI video detectors exploit motion biases in evaluation datasets and drop to near-random performance on rebalanced data, while frequency-based detectors remain robust.
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Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Reference-frame dominance in self-attention suppresses motion in image-to-video models; DyMoS rebalances attention from generated frames to the reference during initial denoising steps to improve dynamics while preserving fidelity.