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DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model (2405.02696v2)

Published 4 May 2024 in cs.CR and cs.AI

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 contents. However, post-processed watermarking methods are unable to withstand generative watermark attacks and there exists a trade-off between image fidelity and watermark strength. Therefore, we propose a novel technique called DiffuseTrace. DiffuseTrace does not rely on fine-tuning of the diffusion model components. The multi-bit watermark is a embedded into the image space semantically without compromising image quality. The watermark component can be utilized as a plug-in in arbitrary diffusion models. We validate through experiments the effectiveness and flexibility of DiffuseTrace. Under 8 types of image processing watermark attacks and 3 types of generative watermark attacks, DiffuseTrace maintains watermark detection rate of 99% and attribution accuracy of over 94%.

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Citations (6)
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Summary

  • The paper introduces a unified representation that embeds watermark information into initial latent variables for robust traceability.
  • It integrates watermarking within the sampling process to maintain image quality while resisting removal attacks.
  • The method operates with arbitrary diffusion models without retraining, as validated through extensive experiments.

The paper "DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model" addresses the challenges involved in watermarking outputs from Latent Diffusion Models (LDMs). These models are powerful tools for generating AI content but raise concerns regarding unauthorized use and copyright infringement. Traditional watermarking methods can be evaded, and current approaches allow only fixed message embeddings, necessitating retraining for any modifications.

Key Contributions:

  1. Unified Representation: DiffuseTrace introduces a novel approach that encodes watermark information into the initial latent variables, ensuring that all generated images carry invisible watermarks that can be detected later. This is achieved by training an encoder-decoder model, where the encoder embeds watermark information through the model's initial latent variables.
  2. Sampling Integration: During the sampling process, watermark information is integrated, maintaining the semantic integrity of the watermark without degrading image quality.
  3. Robust Extraction: The method reverses the diffusion process and employs the decoder to extract watermark information, offering robustness against watermark removal techniques that utilize frameworks like variational autoencoders and diffusion models.
  4. Model Compatibility: DiffuseTrace is designed to be compatible with arbitrary diffusion models as a module, without requiring modifications to the core components of the diffusion models. This means the watermark can be embedded and extracted without fine-tuning the underlying model, making the approach flexible and adaptable.
  5. Experimental Validation: Through extensive experiments, the paper demonstrates DiffuseTrace’s effectiveness in maintaining watermark integrity while resisting contemporary attacks aimed at removing watermarks. This makes it a promising tool in the effort to track AI-generated content.

Overall, DiffuseTrace offers a significant advancement in watermarking techniques by providing a flexible, robust, and efficient method to ensure the traceability and integrity of content generated by diffusion models, catering to evolving security needs in generative AI.

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