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

arXiv: https://arxiv.org/abs/2609.23345 · HTML: https://mlsanity.com/paper/2609.23345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
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- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images](https://mlsanity.com/paper/2304.13023/) (2023)
- [AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream Network](https://mlsanity.com/paper/2306.07005/) (2023)
- [Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs](https://mlsanity.com/paper/2506.07045/) (2025)
- [Evaluating and Predicting Distorted Human Body Parts for Generated Images](https://mlsanity.com/paper/2503.00811/) (2025)
- [AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era](https://mlsanity.com/paper/2504.11015/) (2025)
- [Similarity Trajectories: Linking Sampling Process to Artifacts in Diffusion-Generated Images](https://mlsanity.com/paper/2412.17109/) (2024)
- [Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generation](https://mlsanity.com/paper/2510.16319/) (2025)

## Abstract (arXiv)

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.