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We study statistical watermarking by formulating it as a hypothesis testing problem, a general framework which subsumes all previous statistical watermarking methods.
The existence of probability measures with given marginals
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Bounds for entropy and divergence for distributions over a two-element set
Flemming Topsøe · 2001
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Watermarking the outputs of structured prediction with an application in statistical machine translation
Ashish Venugopal, Jakob Uszkoreit, David Talbot, Franz Och, and Juri Ganitkevitch · 2011
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A review of text watermarking: theory, methods, and applications
Nurul Shamimi Kamaruddin, Amirrudin Kamsin, Lip Yee Por, and Hameedur Rahman · 2018
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Fine-grain watermarking for intellectual property protection
Stefano Giovanni Rizzo, Flavio Bertini, and Danilo Montesi · 2019
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Adversarial watermarking transformer: Towards tracing text provenance with data hiding
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AI-generated answers temporarily banned on coding q&a site stack overflow
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Scott Aaronson · 2023
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Undetectable watermarks for language models
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Publicly detectable watermarking for language models
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Three bricks to consolidate watermarks for large language models
Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon · 2023
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Yu Fu, Deyi Xiong, and Yue Dong · 2023
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Embarrassingly simple text watermarks
Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, and Makoto Yamada · 2023
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The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu · 2023
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Towards codable text watermarking for large language models
Lean Wang, Wenkai Yang, Deli Chen, Hao Zhou, Yankai Lin, Fandong Meng, Jie Zhou, and Xu Sun · 2023
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Ryuto Koike, Masahiro Kaneko, and Naoaki Okazaki · 2023
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Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang · 2023
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A private watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shu’ang Li, Lijie Wen, Irwin King, and Philip S Yu · 2023
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Mark my words: Analyzing and evaluating language model watermarks
Julien Piet, Chawin Sitawarin, Vivian Fang, Norman Mu, and David Wagner · 2023
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Watermarking gpt outputs
Scott Aaronson
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A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein
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On the reliability of watermarks for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein
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Gpt-4 technical report, 2023a
OpenAI
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Borui Yang, Wei Li, Liyao Xiang, and Bo Li · 2023
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Advancing beyond identification: Multi-bit watermark for language models
KiYoon Yoo, Wonhyuk Ahn, and Nojun Kwak · 2023
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Watermarks in the sand: Impossibility of strong watermarking for generative models
Hanlin Zhang, Benjamin L Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak · 2023
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Provable robust watermarking for ai-generated text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, and Yu-Xiang Wang · 2023
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