AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
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| Format: | Preprint |
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2025
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| _version_ | 1866915299783081984 |
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| author | Zhu, Chenyang Zhang, Xing Sun, Yuyang Chang, Ching-Chun Echizen, Isao |
| author_facet | Zhu, Chenyang Zhang, Xing Sun, Yuyang Chang, Ching-Chun Echizen, Isao |
| contents | Recent advances in image generation, particularly diffusion models, have significantly lowered the barrier for creating sophisticated forgeries, making image manipulation detection and localization (IMDL) increasingly challenging. While prior work in IMDL has focused largely on natural images, the anime domain remains underexplored-despite its growing vulnerability to AI-generated forgeries. Misrepresentations of AI-generated images as hand-drawn artwork, copyright violations, and inappropriate content modifications pose serious threats to the anime community and industry. To address this gap, we propose AnimeDL-2M, the first large-scale benchmark for anime IMDL with comprehensive annotations. It comprises over two million images including real, partially manipulated, and fully AI-generated samples. Experiments indicate that models trained on existing IMDL datasets of natural images perform poorly when applied to anime images, highlighting a clear domain gap between anime and natural images. To better handle IMDL tasks in anime domain, we further propose AniXplore, a novel model tailored to the visual characteristics of anime imagery. Extensive evaluations demonstrate that AniXplore achieves superior performance compared to existing methods. Dataset and code can be found in https://flytweety.github.io/AnimeDL2M/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_11015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era Zhu, Chenyang Zhang, Xing Sun, Yuyang Chang, Ching-Chun Echizen, Isao Computer Vision and Pattern Recognition Recent advances in image generation, particularly diffusion models, have significantly lowered the barrier for creating sophisticated forgeries, making image manipulation detection and localization (IMDL) increasingly challenging. While prior work in IMDL has focused largely on natural images, the anime domain remains underexplored-despite its growing vulnerability to AI-generated forgeries. Misrepresentations of AI-generated images as hand-drawn artwork, copyright violations, and inappropriate content modifications pose serious threats to the anime community and industry. To address this gap, we propose AnimeDL-2M, the first large-scale benchmark for anime IMDL with comprehensive annotations. It comprises over two million images including real, partially manipulated, and fully AI-generated samples. Experiments indicate that models trained on existing IMDL datasets of natural images perform poorly when applied to anime images, highlighting a clear domain gap between anime and natural images. To better handle IMDL tasks in anime domain, we further propose AniXplore, a novel model tailored to the visual characteristics of anime imagery. Extensive evaluations demonstrate that AniXplore achieves superior performance compared to existing methods. Dataset and code can be found in https://flytweety.github.io/AnimeDL2M/. |
| title | AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.11015 |