Year: 2025
Type: conference-paper
Abstract: As artificial intelligence surpasses human capabilities in text generation, the necessity to authenticate the origins of AI-generated content has become paramount.Unbiased watermarks offer a powerful solution by embedding statistical signals into language model-generated text without distorting the quality.In this paper, we introduce MCMARK, a family of unbiased, Multi-Channel-based watermarks.MCMARK works by partitioning the model's vocabulary into segments and promoting token probabilities within a selected segment based on a watermark key.We demonstrate that MCMARK not only preserves the original distribution of the language model but also offers significant improvements in detectability and robustness over existing unbiased watermarks.Our experiments with widely-used language models demonstrate an improvement in detectability of over 10% using MCMARK, compared to existing state-of-the-art unbiased watermarks.This advancement underscores MCMARK's potential in enhancing the practical application of watermarking in AI-generated texts.
Authors: Ruibo Chen, Yihan Wu, Junfeng Guo, Heng Huang
Institution: University of Maryland, College Park
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: III: Medium: Collaborative Research: Integrating Large-Scale Machine Learning and Edge Computing for Collaborative Autonomous Vehicles, Collaborative Research: CCRI: New: A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning, A New Machine Learning Framework for Single-Cell Multi-Omics Bioinformatics, Collaborative Research: PPoSS: LARGE: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems, Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design
OpenAlex ID: W4412944931