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.

翻译：潜在扩散模型（LDM）虽赋能广泛应用，却也引发了关于非法利用的伦理担忧。向生成模型输出添加水印是用于版权追踪并缓解AI生成内容潜在风险的关键技术。然而，事后水印技术易被规避。现有针对LDM的水印方法仅能嵌入固定信息，更改水印信息需重新训练模型，且水印稳定性受模型更新迭代影响。此外，当前基于变分自编码器（VAE）和扩散模型的重构式水印去除技术能够移除大部分水印。为此，我们提出一种名为DiffuseTrace的新技术，旨在语义层面将不可见水印嵌入所有生成图像以供未来检测。该方法通过训练编码器-解码器模型建立初始潜变量与水印信息的统一表示，利用编码器将水印信息嵌入初始潜变量并融入采样过程，再通过逆向扩散过程结合解码器提取水印。DiffuseTrace无需对扩散模型组件进行微调，将水印语义嵌入图像空间而不损害图像质量，编码器-解码器可作为插件应用于任意扩散模型。实验验证了DiffuseTrace的有效性与灵活性，该技术在对抗基于变分自编码器和扩散模型的最新攻击时具有前所未有的优势。