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About this paper
Zhipei Xu, Xuanyu Zhang, Youmin Xu, Qing Huang, Shen Chen, Taiping Yao, Shouhong Ding, Jian Zhang
GenShield presents a unified autoregressive framework that performs explainable AI-generated image detection and controllable artifact correction. This system, trained with a Visual Chain-of-Thought curriculum, consistently improves detection accuracy to 98.8% on Holmes-Set and achieves superior artifact removal, reducing mean artifact scores to 0.15 on SynthScars.
Key insights
- A mutually reinforcing relationship exists between AIGI detection and artifact correction, where insights from one can guide and improve the other.
- An iterative, self-explained "diagnose-then-repair" process, enabled by Visual Chain-of-Thought, is essential for effective and controllable artifact correction.
- Unified understanding-generation architectures can provide a more robust and explainable approach to AI image forensics compared to fragmented methods.
Summary by alphaXiv.
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