Warfare:Breaking the Watermark Protection of AI-Generated Content

• URL: http://arxiv.org/abs/2310.07726v3
• Date: Fri, 8 Mar 2024 08:58:10 GMT
• Title: Warfare:Breaking the Watermark Protection of AI-Generated Content
• Authors: Guanlin Li, Yifei Chen, Jie Zhang, Jiwei Li, Shangwei Guo, Tianwei Zhang
• Abstract summary: A promising solution to achieve this goal is watermarking, which adds unique and imperceptible watermarks on the content for service verification and attribution. We show that an adversary can easily break these watermarking mechanisms. We propose Warfare, a unified methodology to achieve both attacks in a holistic way.
• Score: 33.997373647895095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-Generated Content (AIGC) is gaining great popularity, with many emerging commercial services and applications. These services leverage advanced generative models, such as latent diffusion models and large language models, to generate creative content (e.g., realistic images and fluent sentences) for users. The usage of such generated content needs to be highly regulated, as the service providers need to ensure the users do not violate the usage policies (e.g., abuse for commercialization, generating and distributing unsafe content). A promising solution to achieve this goal is watermarking, which adds unique and imperceptible watermarks on the content for service verification and attribution. Numerous watermarking approaches have been proposed recently. However, in this paper, we show that an adversary can easily break these watermarking mechanisms. Specifically, we consider two possible attacks. (1) Watermark removal: the adversary can easily erase the embedded watermark from the generated content and then use it freely bypassing the regulation of the service provider. (2) Watermark forging: the adversary can create illegal content with forged watermarks from another user, causing the service provider to make wrong attributions. We propose Warfare, a unified methodology to achieve both attacks in a holistic way. The key idea is to leverage a pre-trained diffusion model for content processing and a generative adversarial network for watermark removal or forging. We evaluate Warfare on different datasets and embedding setups. The results prove that it can achieve high success rates while maintaining the quality of the generated content. Compared to existing diffusion model-based attacks, Warfare is 5,050~11,000x faster.

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