Diversity-Preserving Robust Watermarking for Diffusion Model Generated Images

This paper introduces a robust watermarking technique for diffusion model-generated images, which effectively balances watermark robustness, image fidelity, and diversity preservation. Unlike traditional post-hoc approaches, the proposed method embeds watermark information directly into the latent noise of the diffusion model, ensuring seamless integration into the image generation process. This approach minimizes perceptual impact while maintaining high visual quality and diversity of the generated images. Experimental results demonstrate the method’s resilience to various image distortions, including noise, compression, blurring etc., significantly outperforming existing watermarking techniques. The proposed method supports both watermark detection and bit-level extraction, providing a practical solution for secure content protection and traceability in generative models without compromising the integrity of the image generation process.