TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors
Recent diffusion models have achieved remarkable realism in facial image synthesis, posing growing challenges to artificial intelligence-generated content (AIGC) forensic detectors.Existing evasion methods typically perturb pre-generated images or require detector-aware training, which may introduce visible or statistical artifacts and limit applicability when the diffusion model must remain frozen and the target detector is accessible only through black-box queries. We propose Trajectory-Injected Generative Attack (TIGA), a source-image-free and training free framework that generates detector-evasive images within a single diffusion sampling trajectory. TIGA steers the latent Denoising D...
| Authors | Xia Du, Zhuosen Bao, Zheng Lin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 2 September 2026 |
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