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k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
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Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to paraphrase attacks, SemStamp (Hou et al., 2023) applies watermark on the semantic representation of sentences and demonstrates promising robustness. SemStamp employs locality-sensitive hashing (LSH) to partition the semantic space with arbitrary hyperplanes, which results in a suboptimal tradeoff between robustness and speed. We propose k-SemStamp, a simple yet effective enhancement of SemStamp, utilizing k-means clustering as an alternative of LSH to partition the embedding space with awareness of inherent semantic structure. Experimental results indicate that k-SemStamp saliently improves its robustness and sampling efficiency while preserving the generation quality, advancing a more effective tool for machine-generated text detection.
Forward citations
Cited by 4 Pith papers
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A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.
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SAMark uses self-anchored semantic green regions, multi-channel hyperbolic scoring, and diversity-aware filtering to reach 90.2% TP@FP1% detection under paragraph paraphrasing while preserving text quality.
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CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality
CoheMark embeds a sentence-level watermark by requiring sampled sentences to pass fuzzy cluster-membership criteria, achieving TPR@1% above 97% with quality judged better than baselines by GPT-4o.
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SoK: Watermarking for AI-Generated Content
A systematization of knowledge on watermarking for AI-generated content, unifying definitions, threat models, evaluation methods, and representative schemes across modalities.
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