In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content. We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a benchmark for assessing image watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks and establishes a standardized evaluation protocol comprised of a diverse range of stress tests. The attacks in WAVES range from traditional image distortions to advanced, novel variations of diffusive, and adversarial attacks. Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks. Our novel, comprehensive evaluation reveals previously undetected vulnerabilities of several modern watermarking algorithms. We envision WAVES as a toolkit for the future development of robust watermarks. The project is available at https://wavesbench.github.io/
Southern University of Science and Technology（南方科技大学）；Hunan University（湖南大学）；Changsha University of Science and Technology（长沙理工大学）
Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DBMARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible…
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
University of California, Santa Cruz（加州大学圣克鲁兹分校）；The University of Hong Kong（香港大学）；University of Science and Technology of China（中国科学技术大学）；IEG, Tencent（腾讯IEG）；Fudan University（复旦大学）
University of Nebraska at Omaha（内布拉斯加大学奥马哈分校）；New Jersey Institute of Technology（新泽西理工学院）
Digital image watermarking is the process of embedding and extracting watermark covertly on a carrier image. Incorporating deep learning networks with image watermarking has attracted increasing attention during recent years. However, existing deep learning-based watermarking systems cannot achieve robustness, blindness, and automated embedding and extraction simultaneously. In this paper, a fully automated image watermarking system based on deep neural networks is proposed to generalize the image watermarking processes. An unsupervised deep learning structure and a novel loss computation are …
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）
A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior
Electrical and Computer Engineering University of Minnesota, Twin Cities（电气与计算机工程大学明尼苏达大学，双城分校）；Computer Science and Engineering University of Minnesota, Twin Cities（计算机科学与工程大学明尼苏达大学，双城分校）
Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing. Traditional image watermarking solutions easily suffer from robustness when specified with some prior constraints, while recent deep learning-based watermarking methods could not tackle the information loss problem well under various separate pipelines of feature encoder and decoder…
Learning Generalizable and Efficient Image Watermarking via Hierarchical Two-Stage Optimization
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（深圳先进研究院，电子科学与技术大学）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
NIT Silchar（西尔恰尔国家理工学院）；IIIT Delhi（德里英迪拉·甘地信息技术学院）；BITS Pilani Hyderabad（博拉理工学院海得拉巴校区）；Meta AI；Stanford University（斯坦福大学）；Amazon GenAI（亚马逊生成式人工智能）；AI Institute, University of South Carolina（南卡罗来纳大学人工智能研究院）
One of the effective methods for the preservation of copyright ownership of digital media is watermarking. Different watermarking techniques try to set a tradeoff between robustness and transparency of the process. In this research work, we have used color space conversion and frequency transform to achieve high robustness and transparency. Due to the distribution of image information in the RGB domain, we use the YUV color space, which concentrates the visual information in the Y channel. Embedding of the watermark is performed in the DCT coefficients of the specific wavelet subbands. Experim…
Capacity, Robustness, & Perceptual quality of watermark data are very important issues to be considered. A lot of research is going on to increase these parameters for watermarking of the digital images, as there is always a tradeoff among them. . In this paper an efficient watermarking algorithm to improve payload and robustness without affecting perceptual quality of image data based on DWT is discussed. The aim of the paper is to employ the nested watermarks in wavelet domain which increases the capacity and ultimately the robustness against attacks and selection of different scaling factor…
GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Towards Robust Content Watermarking Against Removal and Forgery Attacks
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Waterloo（滑铁卢大学）
本文提出Instance-Specific watermarking with Two-Sided detection方法，通过动态控制水印注入时间和模式提升鲁棒性，有效对抗移除和伪造攻击。
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）；Seattle University（西雅图大学）
With the growth of editing and sharing images through the internet, the importance of protecting the images' authorship has increased. Robust watermarking is a known approach to maintaining copyright protection. Robustness and imperceptibility are two factors that are tried to be maximized through watermarking. Usually, there is a trade-off between these two parameters. Increasing the robustness would lessen the imperceptibility of the watermarking. This paper proposes an adaptive method that determines the strength of the watermark embedding in different parts of the cover image regarding its…
SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models
School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；Anhui Province Key Laboratory of Digital Security and the CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China（安徽省数字安全重点实验室和中国科学院电磁空间信息重点实验室，中国科学技术大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
T2SMark: Balancing Robustness and Diversity in Noise-as-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）；National University of Singapore（新加坡国立大学）
School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）
School of Computer Science, Beijing Institute of Technology（北京理工大学计算机学院）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳北理莫斯科大学人工智能研究院）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电气工程与计算机科学学院计算机系）
