Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.
Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localization. We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask. The perturbation made by watermark embedding is close to imperceptible. Considering that there is no off-the-shelf different…
WMCopier: Forging Invisible Image Watermarks on Arbitrary Images
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks
School of Cyber Science and Technology, University of Science and Technology of China, Anhui, China（中国科学技术大学网络安全学院）；Anhui Province Key Laboratory of Digital Security, Anhui, China（安徽省数字安全重点实验室）
Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Delft University of Technology（代尔夫特理工大学）；The Chinese University of Hong Kong（香港中文大学）；University of Neuchâtel（Neuchâtel大学）；University of Turin（都灵大学）；IBM Research（IBM研究院）
DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning
The Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT), Queensland University of Technology, Australia（信号处理、人工智能与视觉技术研究所（SAIVT），昆士兰理工大学）
Digital Photo images are everywhere around us in journals, on walls, and over the Internet. However we have to be conscious that seeing does not always imply reality. Photo images become a rich subject of manipulations due to the advanced digital cameras as well as photo editing software. Accordingly, image forgery is becoming much easier using the existing tools in terms of time and accuracy, and thus the forensics of detecting an image forgery case is becoming difficult and needs more and more time and techniques to prove the image originality especially as crime evidences and court related …
CEFW: A Comprehensive Evaluation Framework for Watermark in Large Language Models
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Hong Kong University of Science and Technology（香港科技大学）
TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
Economics and Management School, Wuhan University（武汉大学经济管理学院）；School of Economics and Management, Tsinghua University（清华大学经济管理学院）；School of Business Administration, Southwestern University of Finance and Economics（西南财经大学商学院）
Unforgeable Watermarks for Language Models via Robust Signatures
Paul G. Allen School of Computer Science & Engineering, University of Washington（保罗·G·艾伦计算机科学与工程学院，华盛顿大学）；Data Science Institute, University of Chicago（数据科学研究所，芝加哥大学）
Digital watermarking is a remarkable issue in the field of information security to avoid the misuse of images in multimedia networks. Although access to unauthorized persons can be prevented through cryptography, it cannot be simultaneously used for copyright protection or content authentication with the preservation of image integrity. Hence, this paper presents an optimized multipurpose blind watermarking in Shearlet domain with the help of smart algorithms including MLP and NSGA-II. In this method, four copies of the robust copyright logo are embedded in the approximate coefficients of Shea…
ComMark: Covert and Robust Black-Box Model Watermarking with Compressed Samples
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；College of Computer Science, Nankai University（南开大学计算机学院）
Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models
University of Science and Technology of China（中国科学技术大学）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）；Department of Information Engineering and Mathematics（信息工程与数学系）；University of Siena（锡耶纳大学）；School of Computing（计算学院）；National University of Singapore（新加坡国立大学）
Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）
