AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning
As generative AI becomes increasingly used in anime-style image creation, distinguishing human-drawn, AI-inpainted, and text-to-image images is important for copyright attribution, visual provenance, and content governance. Existing AI-generated image detectors mainly target real-world photographs and often overlook anime-specific cues such as flat coloring, exaggerated structures, and artistic line control. To address this gap, we propose AniPrO, a multi-dimensional description-enhanced framework for interpretable anime image provenance. Built upon AnimeDL-2M, AniPrO contains 15,000 balanced samples from a 35,000-image candidate pool, covering Real, Inpainting, and Text2Image categories ...
| Authors | Yan Liu, Baoxiang Huang, Zi'an Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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- YAN-LIU05/AniProDataset
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