
BD
Benjamin D. Kim, Wanrong Zhang, Weitong Ruan, Lav R. Varshney, Daniel Alabi
· 1 min read
ResearcharXiv cs.AI
SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints
arXiv:2610.05712v1 Announce Type: cross
Abstract: We introduce a framework for multi-bit text watermarking with security defined directly through $f$-divergence from the base language model distribution. Unlike prior approaches that focus on average-key distortion-freeness or a particular statistical distance, our formulation supports general $f$-divergences, including total variation and KL divergence, and enforces the guarantee for each realized key and embedded message. We develop a coding-based watermarking scheme that optimally biases next-token distributions subject to a prescribed divergence budget, and characterize the resulting tradeoff between embedding rate, decoding reliability, and statistical security. Experimentally, we compare our method against prior multi-bit watermarking schemes across modern language models and payload regimes. Our approach achieves substantially lower watermark detectability while maintaining competitive message-recovery performance and generation quality. Our results provide a unified view of secure multi-bit watermarking and recover several commonly used security notions as special cases.
Original source
This story was published by arXiv cs.AI and written by Benjamin D. Kim, Wanrong Zhang, Weitong Ruan, Lav R. Varshney, Daniel Alabi. SyncAI.news shows a preview; the complete article is on the publisher's site.
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