DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model
arxiv(2024)
摘要
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.
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