Masking-Watermarking Cooperative: An End-to-End Adversarial Protection Framework for Secure Text Distribution and Sharing

In recent years, the frequent leakage of sensitive information in natural language texts has presented significant challenges in text distribution, sharing, and migration. Existing methods for text protection and watermarking are limited, capable of either hide sensitive data or prevent unauthorized copying and tampering, but not both simultaneously. To address these limitations, this paper proposes a masking-watermarking cooperative framework designed to hide text-sensitive information, prevent unintentional data leakage, and ensure content ownership verification and tampering prevention. The framework introduces three novel techniques: a variable autoencoder to ensure diverse watermark generation, an improved transformer to enhance the adaptability of dynamic masks, and a dual discriminator for joint verification of text and watermarks. A comprehensive evaluation was conducted, covering text similarity, masking flexibility, and the imperceptibility of text masking, as well as watermark classification recognition and robustness against various attacks. The proposed framework achieved a score of 0.98 on the SBERT metric, demonstrating its effectiveness in achieving imperceptible text masking and robust watermark embedding.