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AI-Generated Content (AIGC) is gaining great popularity, with many emergingcommercial services and applications. These services leverage advancedgenerative models, such as latent diffusion models and large language models,to generate creative content (e.g., realistic images and fluent sentences) forusers. The usage of such generated content needs to be highly regulated, as theservice providers need to ensure the users do not violate the usage policies(e.g., abuse for commercialization, generating and distributing unsafecontent). A promising solution to achieve this goal is watermarking, which addsunique and imperceptible watermarks on the content for service verification andattribution. Numerous watermarking approaches have been proposed recently.However, in this paper, we show that an adversary can easily break thesewatermarking mechanisms. Specifically, we consider two possible attacks. (1)Watermark removal: the adversary can easily erase the embedded watermark fromthe generated content and then use it freely bypassing the regulation of theservice provider. (2) Watermark forging: the adversary can create illegalcontent with forged watermarks from another user, causing the service providerto make wrong attributions. We propose Warfare, a unified methodology toachieve both attacks in a holistic way. The key idea is to leverage apre-trained diffusion model for content processing and a generative adversarialnetwork for watermark removal or forging. We evaluate Warfare on differentdatasets and embedding setups. The results prove that it can achieve highsuccess rates while maintaining the quality of the generated content. Comparedto existing diffusion model-based attacks, Warfare is 5,050~11,000x faster.

Warfare:Breaking the Watermark Protection of AI-Generated Content