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Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications.
Statistical theory of extreme values and some practical applications: a series of lectures , volume 33
Emil Julius Gumbel · 1948
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Chosen ciphertext attacks against protocols based on the rsa encryption standard pkcs# 1
Daniel Bleichenbacher · 1998
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Watermark estimation through detector analysis
Ton Kalker, J-P Linnartz, and Marten van Dijk · 1998
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Analysis of the sensitivity attack against electronic watermarks in images
Jean Paul MG Linnartz and Marten Van Dijk · 1998
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Design and analysis of practical public-key encryption schemes secure against adaptive chosen ciphertext attack
Ronald Cramer and Victor Shoup · 2003
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On reliability and security of randomized detectors against sensitivity analysis attacks
Maha El Choubassi and Pierre Moulin · 2009
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Are you threatening me?: Towards smart detectors in watermarking
Mauro Barni, Pedro Comesaña-Alfaro, Fernando Pérez-González, and Benedetta Tondi · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer · 2018
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Paraphrase generation with deep reinforcement learning
Zichao Li, Xin Jiang, Lifeng Shang, and Hang Li · 2018
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Release strategies and the social impacts of language models, 2019
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, and Jasmine Wang · 2019
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The harmonic mean p-value for combining dependent tests
Daniel J Wilson · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Towards document-level paraphrase generation with sentence rewriting and reordering
Zhe Lin, Yitao Cai, and Xiaojun Wan · 2021
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Chatgpt: Optimizing language models for dialogue
OpenAI · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Watermarking of large language models
Scott Aaronson · 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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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 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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Miranda Christ, Sam Gunn, and Or Zamir · 2023
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Publicly detectable watermarking for language models
Jaiden Fairoze, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, and Mingyuan Wang · 2023
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On the learnability of watermarks for language models
Chenchen Gu, Xiang Lisa Li, Percy Liang, and Tatsunori Hashimoto · 2023
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Unbiased watermark for large language models
Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, and Heng Huang · 2023
Cited alongside, same era.
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Frederick Wieting, and Mohit Iyyer · 2023
Cited alongside, same era.
Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang · 2023
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Detectgpt: zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn · 2023
Cited alongside, same era.
Yuxin Wen, John Kirchenbauer, Jonas Geiping, and Tom Goldstein · 2023
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Dipmark: A stealthy, efficient and resilient watermark for large language models
Yihan Wu, Zhengmian Hu, Hongyang Zhang, and Heng Huang · 2023
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Watermarks in the sand: Impossibility of strong watermarking for generative models
Hanlin Zhang, Benjamin Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak · 2023
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Pseudorandom error-correcting codes
Miranda Christ and Sam Gunn · 2024
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Watermark stealing in large language models
Nikola Jovanović, Robin Staab, and Martin Vechev · 2024
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A semantic invariant robust watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng, and Lijie Wen · 2024
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A robust semantics-based watermark for large language model against paraphrasing
Jie Ren, Han Xu, Yiding Liu, Yingqian Cui, Shuaiqiang Wang, Dawei Yin, and Jiliang Tang · 2024
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Gaussian shading: Provable performance-lossless image watermarking for diffusion models
Zijin Yang, Kai Zeng, Kejiang Chen, Han Fang, Weiming Zhang, and Nenghai Yu · 2024
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Provable robust watermarking for AI-generated text
Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, and Yu-Xiang Wang · 2024
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