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

2026-09-20

AI Summary

为解决动漫图像来源鉴别问题，提出AniPrO框架，通过多维度语义推理提高对AI生成、人工绘制及修复图像的识别能力。

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.

Problem

• Anime Image
• Provenance Detection
• Generative AI
• Semantic Reasoning
• Copyright Attribution

Innovation

• Interpretable Anime Image Provenance
• Multi-Dimensional Semantic Reasoning
• AnimeDL-2M
• AniPrO-SFD-Bench
• AniPrO-MFR-Bench