Watermarking techniques offer a promising way to secure data via embedding covert information into the data. A paramount challenge in the domain lies in preserving the distribution of original data during watermarking. Our research extends and refines existing watermarking framework, placing emphasis on the importance of a distribution-preserving (DiP) watermark. Contrary to the current strategies, our proposed DiPmark preserves the original token distribution during watermarking (stealthy), is detectable without access to the language model API or weights (efficient), and is robust to moderate changes of tokens (resilient). This is achieved by incorporating a novel reweight strategy, combined with a hash function that assigns unique \textit{i.i.d.} ciphers based on the context. The empirical benchmarks of our approach underscore its stealthiness, efficiency, and resilience, making it a robust solution for watermarking tasks that demand impeccable quality preservation.

翻译：水印技术通过向数据中嵌入隐藏信息，为数据安全提供了有效途径。该领域的核心挑战在于如何在嵌入水印过程中保持原始数据分布。本研究对现有水印框架进行了扩展与优化，强调保持分布（DiP）水印的重要性。与现有策略不同，我们提出的DiPmark在嵌入水印时能保持原始词元分布（隐蔽性），无需访问语言模型API或权重即可检测（高效性），并且对词元的适度篡改具有鲁棒性（鲁棒性）。这一成果通过创新性的重加权策略实现，该策略结合了基于上下文分配独立同分布（i.i.d.）密码的哈希函数。实验基准测试表明，该方法在隐蔽性、高效性和鲁棒性方面均表现优异，成为需要保持完美质量的水印任务的稳健解决方案。