arXiv:2608. 11643v1 Announce Type: cross Abstract: Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork.
By Shivank Singh Thakur, Meien Li, Mark Stamp
arXiv:2606. 09909v1 Announce Type: cross Abstract: With the growing concerns over copyright infringement in diffusion-based customization, adversarial attacks have emerged as a prominent defense strategy to prevent malicious content forgery in personalized image generation.
By Ziang Xu, Wenbo Yu, Hongyao Yu, Hao Fang, Jiawei Kong, Bin Chen, Hao Wu, Shu-Tao Xia, Zhiyong Wu
arXiv:2502. 19716v3 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source content.
By Qijie Xu, Can Wang, Jiawei Chen, Siwei Lyu, Defang Chen
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:2410. 01574v4 Announce Type: replace-cross Abstract: The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse.
By Sina Mavali, Jonas Ricker, David Pape, Asja Fischer, Lea Sch\"onherr
The paper introduces TIGA, a source‑image‑free, training‑free attack that injects adversarial properties into a diffusion model’s sampling trajectory to evade black‑box AIGC forensic detectors. TIGA aggregates gradients from white‑box surrogate detectors to create a transferable prior, then uses anisotropic directional search with finite‑difference queries to estimate and stabilize directions for the DDIM trajectory, applying frequency‑domain reshaping to reduce artifacts. Experiments demonstrate strong black‑box attack performance, transferability, and robustness to post‑processing while maintaining high perceptual quality.
By Xia Du, Zhuosen Bao, Zheng Lin, Jizhe Zhou, Chi-man Pun, Jun Luo, Symeon Chatzinotas