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About this paper
Jialiang Shen, Jiyang Zheng, Yunqi Xue, Huajie Chen, Yu Yao, Hui Kang, Ruiqi Liu, Helin Gong and 3 more
The DINO-Detect framework enhances AI-generated image detection by creating blur-robust models through a sharp-to-blur knowledge distillation approach using a DINOv3 backbone. This method achieves state-of-the-art performance across various benchmarks and significantly improves detection accuracy on motion-blurred images, addressing a critical real-world limitation.
Key insights
- Motion blur significantly compromises existing artifact-based AIGI detectors by erasing high-frequency cues, necessitating a shift towards more stable, semantically rich representations.
- Knowledge distillation from a 'sharp' teacher to a 'blur-aware' student effectively transfers discriminative knowledge and enables learning of blur-invariant features from high-capacity vision transformers.
- Enforcing sharp-blur feature alignment and ordinal contrastive distances helps the model maintain consistent structural and semantic understanding, even when pixel-level details are degraded.
Summary by alphaXiv.
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