AliMark: Enhancing Robustness of Sentence-Level Watermarking Against Text Paraphrasing

Researchers introduce AliMark, a novel sentence-level watermarking framework that improves robustness against text paraphrasing by reformulating watermark detection as a bit sequence alignment problem. The approach uses multiple text variants and adaptive alignment strategies to withstand structural perturbations like sentence splitting and merging, substantially outperforming existing methods against strong paraphrasers.

AliMark addresses a critical vulnerability in current text watermarking systems that protect intellectual property and detect AI-generated content. While existing sentence-level watermarking methods anchor marks in semantic meaning, they falter when strong paraphrasers like DIPPER and GPT-3.5 restructure text through splitting or merging sentences—structural changes that preserve meaning while breaking prefix-based watermark designs. This limitation undermines watermarking's practical utility in content authentication and provenance tracking.

The research builds on the broader evolution of digital watermarking for neural text generation, where detection robustness remains a persistent challenge. As large language models proliferate and paraphrasing tools become more sophisticated, watermarking schemes must evolve accordingly. Previous approaches focused exclusively on semantic preservation but overlooked the structural flexibility of human language.

AliMark's innovation lies in its two-stage detection strategy: generating multiple restructured variants of suspect text and adaptively aligning extracted bit sequences with a secret sequence to minimize cost. This multi-candidate approach naturally accommodates sentence boundary changes without sacrificing detection accuracy. The framework reframes watermarking as an information theory problem rather than a semantic one, enabling more resilient detection mechanisms.

The implications extend across content verification, copyright protection, and AI transparency initiatives. As watermarking becomes foundational to responsible AI deployment, robust methods directly enable better detection of machine-generated text and unauthorized content reuse. Enterprise applications relying on watermark verification—from publishing to digital media—benefit from improved reliability. Future work likely focuses on computational efficiency and integration with production-scale watermarking systems.

• →AliMark reformulates watermark detection as bit sequence alignment rather than semantic anchoring, enabling robustness to structural text changes.
• →The two-stage detection strategy using multiple text variants substantially outperforms existing methods against strong paraphrasers.
• →Current watermarking systems fail against sentence splitting and merging operations despite preserving semantic meaning.
• →The framework addresses a critical gap in protecting AI-generated content and verifying text authenticity at scale.
• →Improved watermarking robustness directly supports regulatory compliance and responsible AI deployment initiatives.