Liangqi Lei

Liangqi Lei, Keke Gai, Jing Yu et al.

Watermarking is a technical alternative to safeguarding intellectual property and reducing misuse. Existing methods focus on optimizing watermarked latent variables to balance watermark robustness and fidelity, as Latent diffusion models (LDMs) are considered a powerful tool for generative tasks. However, reliance on computationally intensive heuristic optimization for iterative signal refinement results in high training overhead and local optima entrapment.To address these issues, we propose an \underline{A}na\underline{l}ytical Watermark\underline{i}ng Framework for Controllabl\underline{e} Generatio\underline{n} (ALIEN). We develop the first analytical derivation of the time-dependent modulation coefficient that guides the diffusion of watermark residuals to achieve controllable watermark embedding pattern.Experimental results show that ALIEN-Q outperforms the state-of-the-art by 33.1\% across 5 quality metrics, and ALIEN-R demonstrates 14.0\% improved robustness against generative variant and stability threats compared to the state-of-the-art across 15 distinct conditions. Code can be available at https://anonymous.4open.science/r/ALIEN/.

Liangqi Lei, Keke Gai, Jing Yu et al.

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 Artificial Intelligence (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%.

Liangqi Lei, Keke Gai, Jing Yu et al.

The personalization techniques of diffusion models succeed in generating images with specific concepts. This ability also poses great threats to copyright protection and network security since malicious users can generate unauthorized content and disinformation relevant to a target concept. Model watermarking is an effective solution to trace the malicious generated images and safeguard their copyright. However, existing model watermarking techniques merely achieve image-level tracing without concept traceability. When tracing infringing or harmful concepts, current approaches execute image concept detection and model tracing sequentially, where performance is critically constrained by concept detection accuracy. In this paper, we propose a lightweight concept watermarking framework that efficiently binds target concepts to model watermarks, supporting simultaneous concept identification and model tracing via single-stage watermark verification. To further enhance the robustness of concept watermarking, we propose an adversarial perturbation injection method collaboratively embedded with watermarks during image generation, avoiding watermark removal by model purification attacks. Experimental results demonstrate that ConceptWM significantly outperforms state-of-the-art watermarking methods, improving detection accuracy by 6.3%-19.3% across diverse datasets including COCO and StableDiffusionDB. Additionally, ConceptWM possesses a critical capability absent in other watermarking methods: it sustains a 21.7% FID/CLIP degradation under adversarial fine-tuning of Stable Diffusion models on WikiArt and CelebA-HQ, demonstrating its capability to mitigate model misuse.

Liangqi Lei, Keke Gai, Jing Yu et al.

Latent diffusion models have exhibited considerable potential in generative tasks. Watermarking is considered to be an alternative to safeguard the copyright of generative models and prevent their misuse. However, in the context of model distribution scenarios, the accessibility of models to large scale of model users brings new challenges to the security, efficiency and robustness of existing watermark solutions. To address these issues, we propose a secure and efficient watermarking solution. A new security mechanism is designed to prevent watermark leakage and watermark escape, which considers watermark randomness and watermark-model association as two constraints for mandatory watermark injection. To reduce the time cost of training the security module, watermark injection and the security mechanism are decoupled, ensuring that fine-tuning VAE only accomplishes the security mechanism without the burden of learning watermark patterns. A watermark distribution-based verification strategy is proposed to enhance the robustness against diverse attacks in the model distribution scenarios. Experimental results prove that our watermarking consistently outperforms existing six baselines on effectiveness and robustness against ten image processing attacks and adversarial attacks, while enhancing security in the distribution scenarios.