k-SemStamp: A Clustering-Based Semantic Watermark for Detection of
  Machine-Generated Text
Abe Bohan Hou Jingyu Zhang Yichen Wang Daniel Khashabi Tianxing He
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.