The proliferation of AI-generated images produced by Latent Diffusion Models (LDMs) has raised critical concerns regarding copyright infringement and misinformation. Although existing frequency-domain watermarking methods embed handcrafted geometric patterns into the initial latent noise prior to generation, they suffer from limited capacity and rigid pattern designs.
arXiv:2610.02010v1 Announce Type: cross
Abstract: Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open...
By Kirill Aistov, Khaled Abud, Irina Serzhenko, Egor Kovalev, Aleksey Yakushev, Aleksandr Akimenkov, Dmitry Obydenkov, Yury Markin, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova
arXiv:2608. 10166v1 Announce Type: cross Abstract: Digital watermarking has emerged as a critical technique for provenance and copyright attribution in AI-generated imagery, yet its robustness against realistic, model-agnostic removal attacks remains poorly explored.
By Jie Cao, Qi Li, Zelin Zhang, Xiaodong Wu, Lingshuang Liu, Xiangman Li, Jianbing Ni
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:2609.40031v1 Announce Type: cross
Abstract: Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated co...
By Khaled Abud, Aleksey Yakushev, Aleksandr Akimenkov, Irina Serzhenko, Kirill Aistov, Egor Kovalev, Dmitry Obydenkov, Sergey Lavrushkin, Anastasia Antsiferova, Dmitriy Vatolin, Yury Markin, Kirill Lukianov
Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated content and ensuring traceable sources to prevent man...
The paper introduces VeriFi, a watermarking framework that protects face images from AI‑generated manipulation. It embeds a compact semantic latent watermark to preserve content, localizes pixel‑level edits without explicit payloads, and simulates realistic deepfake attacks to improve robustness. Experiments on CelebA‑HQ and FFHQ show that VeriFi outperforms existing methods in robustness, localization accuracy, and recovery quality.
By Peipeng Yu, Jinfeng Xie, Chengfu Ou, Xiaoyu Zhou, Jianwei Fei, Yunshu Dai, Zhihua Xia, Chip Hong Chang
arXiv:2606. 11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures.
By Jian-Ping Mei, Weibin Zhang, Ao Yao, Tiantian Zhu, Jie Xiao
DRIFT is a black‑box attack that removes diffusion watermarks by deflecting the generative trajectory. It combines partial forward diffusion with stochastic reverse resampling to limit the source information available to a fixed‑depth recovery pipeline and to explore alternative noise‑driven paths. Across nine watermarks, DRIFT achieves 98–100% success while preserving image quality, without requiring secret keys, verifier internals, or per‑image gradient optimization.
By Rui Bao, Zheng Gao, Xiaoyu Li, Xiaoyan Feng, Yang Song, Jiaojiao Jiang
arXiv:2511. 04949v2 Announce Type: replace-cross Abstract: Rapid advances in generative AI have led to increasingly realistic deepfakes, posing growing challenges for law enforcement and public trust.
By Tharindu Fernando, Clinton Fookes, Sridha Sridharan
DRIFT is a black‑box attack that removes diffusion watermarks by combining partial forward diffusion with stochastic reverse resampling. It limits the source information available to a fixed‑depth recovery pipeline and uses stochastic reversal to explore alternative noise‑driven paths, refining fidelity only on updates rejected by the same verifier. Across nine watermarks, DRIFT achieves 98–100% attack success and the best image quality without requiring secret keys, verifier internals, or per‑image gradient optimization.
FeatMark is a watermarking framework that protects images from text‑to‑image diffusion model mimicry attacks by embedding small, scene‑consistent micro‑features instead of pixel‑level perturbations. It constructs domain‑specific feature banks, selects executable features, and injects them via mask‑guided concept editing to create highly localized, natural edits. Experiments on VGGFace2, CelebA‑HQ, and WikiArt show FeatMark remains robust against ten strong watermark removal attacks and several adaptive attacks, with minimal impact on perceptual quality and extending to video mimicry scenarios.
By Haoyang Li, Ruoxi Sun, Qingqing Ye, Benjamin Zi Hao Zhao, Yaxin Xiao, Jason Xue, Haibo Hu