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arXiv · 2609.23345

AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning

Yan Liu·Baoxiang Huang·Zi'an Wang·Wenbo Xie
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Abstract

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 with structured five-dimensional descriptions. We further introduce AniPrO-SFD-Bench and AniPrO-MFR-Bench to evaluate provenance detection from statistical feature discrimination and multimodal fusion reasoning perspectives. Experiments show that structured semantic guidance reveals systematic AI-generation biases, such as the gap between global visual plausibility and local detail coherence, and improves the detection of challenging inpainting samples. The dataset and code will be released at: https://github.com/YAN-LIU05/AniPrO.

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Yan Liu, Baoxiang Huang, Zi'an Wang, Wenbo Xie. 2026-09-20. AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning. https://arxiv.org/abs/2609.23345

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