arXiv Computer Vision
AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning
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arXiv AI
Jul 28
TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2
arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
arXiv Computer Vision
Sep 1
Grounding and Explaining Visual Evidence for AI-Generated Image Detection in Human-Centric Scenes
arXiv:2608.01988v2 Announce Type: replace Abstract: Rapid advances in image generation models call for interpretable AI-generated image detection methods that not only determine authenticity but also...
arXiv AI
Sep 3
Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics
The paper introduces a self‑referential retrosynthesis framework for explainable AI provenance forensics that works with fixed‑generator generative models. It uses a jointly optimized encoder‑decoder pair to embed client inputs, generate high‑fidelity outputs, and then verify provenance by comparing the resynthesized image to the original query. The method eliminates the need for watermarking or generator modifications while providing interpretable evidence of a model’s output origin.
arXiv AI
Jul 15
IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment
arXiv:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.
arXiv Computer Vision
Aug 31
Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art
Abstract4D is the largest dataset of abstract paintings, containing over 120,000 images with rich metadata and multi‑dimensional prompts that capture perceptual attributes such as form, color, texture, and composition. The dataset is annotated via a hybrid human–VLM pipeline to ensure quality and consistency. Using Abstract4D, the authors analyze the semantic structure of abstract art through large‑scale embedding visualization and establish benchmark tasks for classification, cross‑modal retrieval, and text‑to‑image generation to evaluate AI models’ perception and reproduction of abstract visual language.
arXiv AI
Aug 18
Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection
arXiv:2608. 16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable.