Text Watermarking
Overview
Text watermarking studies how to embed signals in language-model outputs so they can be detected later for provenance, attribution, or message recovery. Common approaches bias token probabilities using secret keys, while semantic, structural, and byte-level methods aim to survive paraphrasing, edits, or differences between models; error-correcting codes can help recover longer payloads. Researchers evaluate detection accuracy and false alarms alongside text quality, robustness to attacks, and performance on tasks such as summarization, question answering, and code generation. The central tension is that stronger or more persistent signals can distort output or enable unwanted monitoring, while many current methods remain vulnerable to rewriting and are not yet reliable forensic evidence.
Latest papers
44 papers10 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.