k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
<|reference_start|>k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text: 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.<|reference_end|>
@article{hou2024k-semstamp:,
    title={k-SemStamp: A Clustering-Based Semantic Watermark for Detection of
  Machine-Generated Text},
    author={Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, Tianxing He},
    journal={arXiv preprint arXiv:2402.11399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11399},
    primaryClass={cs.CL cs.CR cs.CY cs.LG}
}