Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
As the misuse of AI-generated images grows, generalizable image detection techniques are urgently needed. Recent state-of-the-art (SOTA) methods adopt aligned training datasets to reduce content, size, and format biases, empowering models to capture robust forgery cues. A common strategy employs reconstruction techniques, e.g., VAE and DDIM, to construct aligned synthetic negatives. However, relying on a single reconstruction process yields a narrow and homogeneous artifact distribution, leaving forensic traces from other artifact-forming mechanisms underrepresented. To broaden artifact coverage without sacrificing alignment, we construct adversarial restoration-based negatives with SRGAN...
| Authors | Yiheng Li, Yang Yang, Wenhao Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 May 2026 |
| Last updated at source | 23 August 2026 |
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