Abstract
Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization.Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with AI-generated content. However, post-hoc watermarking techniques are susceptible to evasion. Existing watermarking methods for LDMs can only embed fixed messages. Watermark message alteration requires model retraining. The stability of the watermark is influenced by model updates and iterations. Furthermore, the current reconstruction-based watermark removal techniques utilizing variational autoencoders (VAE) and diffusion models have the capability to remove a significant portion of watermarks. Therefore, we propose a novel technique called DiffuseTrace. The goal is to embed invisible watermarks in all generated images for future detection semantically. The method establishes a unified representation of the initial latent variables and the watermark information through training an encoder-decoder model. The watermark information is embedded into the initial latent variables through the encoder and integrated into the sampling process. The watermark information is extracted by reversing the diffusion process and utilizing the decoder. DiffuseTrace does not rely on fine-tuning of the diffusion model components. The watermark is embedded into the image space semantically without compromising image quality. The encoder-decoder can be utilized as a plug-in in arbitrary diffusion models. We validate through experiments the effectiveness and flexibility of DiffuseTrace. DiffuseTrace holds an unprecedented advantage in combating the latest attacks based on variational autoencoders and Diffusion Models.
Abstract (translated)
潜在扩散模型（LDMs）允许应用于广泛的领域，但涉及非法利用的伦理问题。在将水印添加到生成模型的输出中是保护版权跟踪和减轻与AI生成的内容相关的潜在风险的重要技术。然而，后置水印技术易被绕过。现有的LDM水印方法只能嵌入固定的消息。水印消息修改需要模型重构。水印的稳定性受模型更新和迭代的影响。此外，使用变分自编码器（VAE）和扩散模型基于重构的消歧水印去除技术具有去除大量水印的能力。因此，我们提出了名为DiffuseTrace的新技术。目标是将不可见的水印嵌入所有生成的图像中，供未来检测具有语义意义。该方法通过训练编码器-解码器模型，将初始潜在变量和 水印信息建立为统一表示。水印信息通过编码器整合到抽样过程中。通过反转扩散过程并利用解码器提取水印信息。DiffuseTrace不依赖于对扩散模型组件的微调。水印在图像空间语义上嵌入，同时不牺牲图像质量。编码器-解码器可以作为任意扩散模型的插件使用。通过实验验证DiffuseTrace的有效性和灵活性。DiffuseTrace在对抗基于变分自编码器（VAE）和扩散模型的最新攻击方面具有史无前例的优势。
URL
https://arxiv.org/abs/2405.02696
