Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles
- URL: http://arxiv.org/abs/2605.06153v1
- Date: Thu, 07 May 2026 12:46:22 GMT
- Title: Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles
- Abstract summary: We argue that the effectiveness of a watermarking scheme should be established purely through a thorough theoretical analysis.<n>This is enabled by decoupling the model-dependent part from the actual decision mechanism of the watermarking system.<n>We propose SSB, a novel watermarking method that generalizes previous seed-based methods by allowing to reach any security-robustness-fidelity regime.
- Score: 3.294420397461204
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid emergence of generative image models has led to the development of specialized watermarking techniques, particularly in-generation methods such as seed-based embedding. However, current evaluations in this area remain largely empirical, making them heavily reliant on the specific model architectures used for generation and inversion. This prevents any clear conclusion on the performance of any method, especially regarding security, for which a rigorous definition is lacking. Against this approach, we argue that the effectiveness of a watermarking scheme should be established purely through a thorough theoretical analysis. This is enabled by decoupling the model-dependent part from the actual decision mechanism of the watermarking system. Using this decoupling, we introduce a formal evaluation framework based on security, robustness, and fidelity. This allows precise comparisons between watermarking systems through a characteristic surface representing the trade-off between these three quantities, independent of any generative model. Based on this framework, we propose SSB, a novel watermarking method that generalizes previous seed-based methods by allowing to reach any security-robustness-fidelity regime on its characteristic surface. This work opens the door to the design of modern watermarking systems with theoretical guarantees that do not necessitate any costly empirical evaluations.
Related papers
- WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models [79.32764976020435]
Digital watermarking is essential for securing generated images from diffusion models.<n>Previous watermark evaluation methods lack a unified framework for both residual and semantic watermarks.<n>We proposeLM, the first unified and interpretable evaluation framework for diffusion model image watermarking via vision-language models.
arXiv Detail & Related papers (2026-01-29T12:14:32Z) - Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models [50.73220224678009]
Watermarking can be used to verify the origin of synthetic images generated by artificial intelligence models.<n>Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial techniques.<n>In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery.<n>Our approach significantly broadens the scope of attacks, presenting a greater challenge to the security of current watermarking techniques.
arXiv Detail & Related papers (2025-06-06T12:08:02Z) - Secure and Efficient Watermarking for Latent Diffusion Models in Model Distribution Scenarios [23.64920988914223]
A new security mechanism is designed to prevent watermark leakage and watermark escape.<n>A watermark distribution-based verification strategy is proposed to enhance the robustness against diverse attacks in the model distribution scenarios.
arXiv Detail & Related papers (2025-02-18T23:55:33Z) - Embedding Watermarks in Diffusion Process for Model Intellectual Property Protection [16.36712147596369]
We introduce a novel watermarking framework by embedding the watermark into the whole diffusion process.
Detailed theoretical analysis and experimental validation demonstrate the effectiveness of our proposed method.
arXiv Detail & Related papers (2024-10-29T18:27:10Z) - Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach [53.32564762183639]
We introduce a novel, unified theoretical framework for watermarking Large Language Models (LLMs)<n>Our approach aims to maximize detection performance while maintaining control over the worst-case false positive rate (FPR) and distortion on text quality.<n>We propose a distortion-free, distribution-adaptive watermarking algorithm (DAWA) that leverages a surrogate model for model-agnosticism and efficiency.
arXiv Detail & Related papers (2024-10-03T18:28:10Z) - Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending [54.26862913139299]
We introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB)<n> TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models.<n>Experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
arXiv Detail & Related papers (2024-09-17T07:52:09Z) - Watermarking Recommender Systems [52.207721219147814]
We introduce Autoregressive Out-of-distribution Watermarking (AOW), a novel technique tailored specifically for recommender systems.
Our approach entails selecting an initial item and querying it through the oracle model, followed by the selection of subsequent items with small prediction scores.
To assess the efficacy of the watermark, the model is tasked with predicting the subsequent item given a truncated watermark sequence.
arXiv Detail & Related papers (2024-07-17T06:51:24Z) - ModelShield: Adaptive and Robust Watermark against Model Extraction Attack [58.46326901858431]
Large language models (LLMs) demonstrate general intelligence across a variety of machine learning tasks.<n> adversaries can still utilize model extraction attacks to steal the model intelligence encoded in model generation.<n> Watermarking technology offers a promising solution for defending against such attacks by embedding unique identifiers into the model-generated content.
arXiv Detail & Related papers (2024-05-03T06:41:48Z) - Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification [46.27328641616778]
We propose a hybrid multiplicative watermarking scheme, where the watermark parameters are periodically updated.
We show that the proposed approach makes it difficult for an eavesdropper to reconstruct the watermarking parameters.
arXiv Detail & Related papers (2023-09-05T16:56:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.