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

2026-09-20 04:15:15
Yan Liu, Baoxiang Huang, Zi'an Wang, Wenbo Xie
arXiv_CV
         
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: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2609.23345

PDF

https://arxiv.org/pdf/2609.23345.pdf

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