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ASAP is an interactive visualization system that helps users identify and analyze deceptive patterns in AI‑generated images. It uses a CLIP‑adapted image encoder to produce interpretable representations and generates masks that highlight influential pixel regions, enabling influence measurement of key deceptive features. The system integrates these techniques into a dashboard for quantifying authenticity‑indicative patterns across collections of authentic and AI‑generated images, supporting comparative analysis of different generative models such as GANs and diffusion models, and its effectiveness is demonstrated through a user study and benchmark applications.
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:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains...
arXiv:2607. 22600v1 Announce Type: new Abstract: Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios, inappropriate encodings, and misleading color mappings-can systematically alter interpretation while preserving the underlying data.
arXiv:2606. 03348v1 Announce Type: cross Abstract: Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility.
arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.