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

2026-02-22

AI Summary

This work proposes SiGRRW, the first single-watermark robust reversible watermarking framework, addressing the limitations of existing dual-watermark two-stage approaches that struggle to simultaneously achieve robustness and reversibility in a single embedding and often suffer from functional interference, leading to reduced capacity and invisibility. SiGRRW introduces a guidance strategy to generate a guidance image and leverages guidance residuals to enable reversible watermark embedding and lossless host recovery. By eliminating interference inherent in dual-watermark schemes, the method preserves perfect reversibility of the host image while significantly enhancing invisibility, robustness, and embedding capacity. Designed as a plug-and-play module, SiGRRW is applicable to both generative model outputs and natural images, with experiments demonstrating its superior performance over state-of-the-art methods.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

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.

Problem

• Robust reversible watermarking
• single-watermark
• imperceptibility
• capacity
• functional interference

Innovation

• Robust Reversible Watermarking
• Single-Watermark Framework
• Guiding Strategy
• Guiding Residual
• Generative Models