ImageAttributionBench: How Far Are We from Generalizable Attribution?
The rapid advancement of generative AI has enabled the creation of highly realistic and diverse synthetic images, posing critical challenges for image provenance and misinformation detection. This underscores the urgent need for effective image attribution. However, existing attribution datasets are constrained by limited scale, outdated generation methods, and insufficient semantic diversity - hindering the development of robust and generalizable attribution models. To address these limitations, we introduce ImageAttributionBench, a comprehensive dataset comprising images synthesized by a wide array of advanced generative models with state-of-the-art (SOTA) architectures. Covering multip...
| Authors | Tingshu Mou, Zhipeng Wei, Chao Gong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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