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.
By Jinbin Huang, Yuki Ueno, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan
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...
By Kun Guo, Yuzhou Yang, Haoyue Wang, Qichao Ying, Sheng Li, Zhenxing Qian
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...
By Alexandros Gazis, Efstathios Karypidis, Kleanthi Santamouri, Theodoros Vavouras, Nikos E. Mastorakis, Stylianos Pappas
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.
By Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman, Mir Tafseer Nayeem, Enamul Hoque
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.
By Junxiao Yang, Minghao Zhang, Xiaoce Wang, Haoran Liu, Shiyao Cui, Hongning Wang, Minlie Huang
arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
By Yijin Wang, Shuyi Wang, Wenhan Zhang, Yuqi Ouyang
arXiv:2606. 19259v1 Announce Type: cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
By Yijin Wang, Shuyi Wang, Wenhan Zhang, Yuqi Ouyang
arXiv:2606. 28386v1 Announce Type: cross Abstract: Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models.
By Bihe Zhao, Louis Kerner, Michel Meintz, Tameem Bakr, Franziska Boenisch, Adam Dziedzic
arXiv:2606. 28510v1 Announce Type: cross Abstract: Across social and online platforms, people are increasingly exposed to AI-generated images.
By Negar Kamali, Candice Rockell Gerstner, Jessica Hullman, Matthew Groh
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.
By Bowen Deng, Jiahui Zhan, Yikun Ji, Haozhen Yan, Jianfu Zhang
arXiv:2603. 28583v2 Announce Type: replace-cross Abstract: Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations.
By Yanjie Zhang, Yafei Li, Rui Sheng, Zixin Chen, Yanna Lin, Huamin Qu, Lei Chen, Yushi Sun
arXiv:2607. 10580v1 Announce Type: cross Abstract: AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthorized facial recognition and targeted advertising.
By Syed Irfan Ali Meerza, Oktay Ozturk, Amir Sadovnik, Jian Liu