Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - The Brittleness of AI-Generated Image Watermarking Techniques: Examining
Their Robustness Against Visual Paraphrasing Attacks
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In response, companies like Meta and\nGoogle have intensified their efforts to implement watermarking techniques on\nAI-generated images to curb the circulation of potentially misleading visuals.\nHowever, in this paper, we argue that current image watermarking methods are\nfragile and susceptible to being circumvented through visual paraphrase\nattacks. The proposed visual paraphraser operates in two steps. First, it\ngenerates a caption for the given image using KOSMOS-2, one of the latest\nstate-of-the-art image captioning systems. Second, it passes both the original\nimage and the generated caption to an image-to-image diffusion system. During\nthe denoising step of the diffusion pipeline, the system generates a visually\nsimilar image that is guided by the text caption. The resulting image is a\nvisual paraphrase and is free of any watermarks. Our empirical findings\ndemonstrate that visual paraphrase attacks can effectively remove watermarks\nfrom images. This paper provides a critical assessment, empirically revealing\nthe vulnerability of existing watermarking techniques to visual paraphrase\nattacks. While we do not propose solutions to this issue, this paper serves as\na call to action for the scientific community to prioritize the development of\nmore robust watermarking techniques. Our first-of-its-kind visual paraphrase\ndataset and accompanying code are publicly available.","upvotes":9,"discussionId":"66c5983e960ea4e66c672d64","ai_summary":"Visual paraphrase attacks using state-of-the-art captioning and diffusion systems can effectively remove watermarks from AI-generated images, highlighting the vulnerability of existing watermarking techniques.","ai_keywords":["KOSMOS-2","image captioning","image-to-image diffusion system","denoising","visual paraphrase attacks","visual paraphrase dataset"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63a4754927f1f64ed7238dac","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg","isPro":false,"fullname":"Aman Chadha","user":"amanchadha","type":"user"},{"_id":"62716952bcef985363db8485","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62716952bcef985363db8485/zJPPo5xlwZRJdEuwYsYKp.jpeg","isPro":true,"fullname":"JB D.","user":"IAMJB","type":"user"},{"_id":"648eb1eb59c4e5c87dc116e0","avatarUrl":"/avatars/c636cea39c2c0937f01398c94ead5dad.svg","isPro":false,"fullname":"fdsqefsgergd","user":"T-representer","type":"user"},{"_id":"65f40e43653c231cbaf7d1e4","avatarUrl":"/avatars/a42ac5454cbe175f04c3420fce90cad2.svg","isPro":false,"fullname":"Jue Zhang","user":"JueZhang","type":"user"},{"_id":"6322d6d8510aa62b8125d755","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6322d6d8510aa62b8125d755/z3qvVBy01iItET9lMmum6.jpeg","isPro":false,"fullname":"Zhiyuan Ma","user":"zhizhi111","type":"user"},{"_id":"641b754d1911d3be6745cce9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/641b754d1911d3be6745cce9/Ydjcjd4VuNUGj5Cd4QHdB.png","isPro":false,"fullname":"atayloraerospace","user":"Taylor658","type":"user"},{"_id":"663ccbff3a74a20189d4aa2e","avatarUrl":"/avatars/83a54455e0157480f65c498cd9057cf2.svg","isPro":false,"fullname":"Nguyen Van Thanh","user":"NguyenVanThanhHust","type":"user"},{"_id":"650fd520be0fdd6ffe85ca64","avatarUrl":"/avatars/57349b18818231eb8df24b3d1c74f5d2.svg","isPro":false,"fullname":"Shashwat Bajpai","user":"Shashwat1729","type":"user"},{"_id":"65aab5ee14d782df06e92037","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/kCr0jRi6X_Bhgw7Tfs5qj.jpeg","isPro":false,"fullname":"Shwetangshu Biswas","user":"shwetangshub","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0}">
Visual paraphrase attacks using state-of-the-art captioning and diffusion systems can effectively remove watermarks from AI-generated images, highlighting the vulnerability of existing watermarking techniques.
AI-generated summary
The rapid advancement of text-to-image generation systems, exemplified by
models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened
concerns about their potential misuse. In response, companies like Meta and
Google have intensified their efforts to implement watermarking techniques on
AI-generated images to curb the circulation of potentially misleading visuals.
However, in this paper, we argue that current image watermarking methods are
fragile and susceptible to being circumvented through visual paraphrase
attacks. The proposed visual paraphraser operates in two steps. First, it
generates a caption for the given image using KOSMOS-2, one of the latest
state-of-the-art image captioning systems. Second, it passes both the original
image and the generated caption to an image-to-image diffusion system. During
the denoising step of the diffusion pipeline, the system generates a visually
similar image that is guided by the text caption. The resulting image is a
visual paraphrase and is free of any watermarks. Our empirical findings
demonstrate that visual paraphrase attacks can effectively remove watermarks
from images. This paper provides a critical assessment, empirically revealing
the vulnerability of existing watermarking techniques to visual paraphrase
attacks. While we do not propose solutions to this issue, this paper serves as
a call to action for the scientific community to prioritize the development of
more robust watermarking techniques. Our first-of-its-kind visual paraphrase
dataset and accompanying code are publicly available.
The paper introduces and empirically demonstrates the vulnerability of current AI-generated image watermarking techniques to visual paraphrasing attacks, calling for the development of more robust solutions.
Introduction of Visual Paraphrase Attack: The paper presents the concept of visual paraphrasing, a novel method to circumvent AI-generated image watermarking techniques by generating visually similar but watermark-free images.
Empirical Evidence of Vulnerability: Through experiments, the authors show that six state-of-the-art watermarking methods are susceptible to these attacks, highlighting their fragility.
Call for Robust Solutions: The study urges the scientific community to develop more resilient watermarking techniques and provides a new dataset and code to benchmark these efforts.