首个百万级动漫图像伪造检测数据集，专为扩散模型时代设计。
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
- 构建超两百万张动漫图像数据集，含真实、部分篡改与全生成样本。
- 现有自然图像检测模型在动漫图像上表现差，存在显著领域差异。
- 提出AniXplore模型，专为动漫视觉特征优化，性能领先。
近期图像生成技术（尤其是扩散模型）的进展大幅降低了伪造图像的制作门槛，使图像篡改检测与定位（IMDL）面临更大挑战。尽管已有研究聚焦于自然图像，但动漫领域仍缺乏探索——尽管其正日益易受AI生成伪造品威胁。将AI生成图像冒充手绘作品、侵犯版权或篡改不当内容等问题，对动漫社区和产业构成严重威胁。为此，我们提出AnimeDL-2M，首个大规模动漫图像IMDL基准，包含超过两百万张图像，涵盖真实、部分篡改及完全生成样本。实验表明，基于自然图像训练的现有模型在动漫图像上表现不佳，凸显两者间的明显领域差异。为此，我们进一步提出AniXplore，一种针对动漫视觉特性的新模型。大量评估显示，AniXplore相比现有方法具有更优性能。数据集与代码见https://flytweety.github.io/AnimeDL2M/。
原文摘要 · Abstract (English)
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/.
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