SiGRRW: A Single-Watermark Robust Reversible Watermarking Framework with Guiding Strategy

TL;DR

This work proposes SiGRRW, a single-watermark RRW framework, which is applicable to both generative models and natural images, and introduces a novel guiding strategy to generate guiding images, serving as the guidance for embedding and recovery.

Abstract

Robust reversible watermarking (RRW) enables copyright protection for images while overcoming the limitation of distortion introduced by watermark itself. Current RRW schemes typically employ a two-stage framework, which fails to achieve simultaneous robustness and reversibility within a single watermarking, and functional interference between the two watermarks results in performance degradation in multiple terms such as capacity and imperceptibility. We propose SiGRRW, a single-watermark RRW framework, which is applicable to both generative models and natural images. We introduce a novel guiding strategy to generate guiding images, serving as the guidance for embedding and recovery. The watermark is reversibly embedded with the guiding residual, which can be calculated from both cover images and watermark images. The proposed framework can be deployed either as a plug-and-play watermarking layer at the output stage of generative models, or directly applied to natural images. Extensive experiments demonstrate that SiGRRW effectively enhances imperceptibility and robustness compared to existing RRW schemes while maintaining lossless recovery of cover images, with significantly higher capacity than conventional schemes.

Figures (8)

• Figure 1: An application scenario of RRW
• Figure 2: The overall architecture and training procedure of the proposed framework
• Figure 3: Gaussian Noise
• Figure 4: Gaussian Blur
• Figure 5: Salt-and-Pepper Noise