arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
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...
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:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.
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: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.
arXiv:2606. 26794v1 Announce Type: cross Abstract: CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment.
arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.
The paper introduces a self-referential retrosynthesis framework for explainable AI provenance forensics that works with fixed generative models. It uses a jointly optimized encoder-decoder pair to embed client inputs, allowing the generator to produce high-fidelity outputs while enabling round-trip consistency checks. The method eliminates the need for watermarking or generator modifications and provides interpretable evidence linking generated images back to their source inputs.
arXiv:2606. 19259v1 Announce Type: cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
arXiv:2608. 14075v1 Announce Type: new Abstract: Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret.
arXiv:2604. 02694v2 Announce Type: replace-cross Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety.