ReMark: Reversible Lexical Substitution-Based Text Watermarking
Ziyu Jiang, Hongxia Wang · 2024
Neural-based natural language watermarking (NLW) shows promise for generating context-aware lexical substitutions, minimizing semantic loss in watermarked text. However, existing works confront two primary challenges: 1) the reliance and sensitivity on textual context during substitutes generation hinders text reversibility, and 2) strict synchronization constraints on the generation order of substitutes from both original and watermarked text blocks out some suitable substitutes, limiting watermark capacity. This paper puts forward a reversible neural NLW approach with improved capacity and text quality. Specifically, we construct a novel lexical substitution system (LSS), utilizing prompt learning for candidates generation and comprehensive assessment features for candidates ranking. A reversible watermarking scheme is then presented by ingeniously screening recoverable positions and enabling multi-bit substitutions via the proposed LSS. Experiments validate that our method achieves complete reversibility while enhancing watermark payload and text fidelity compared to prior arts.