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Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm.
“The Intellectual Challenge of CSCW: The Gap between Social Requirements and Technical Feasibility”
Mark. Ackerman · 2000
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“Natural language watermarking: Design, analysis, and a proof-of-concept implementation”
Mikhail Atallah et al · 2001
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“Linguistic steganography on Twitter: hierarchical language modeling with manual interaction”
Alex Wilson, Phil Blunsom and Andrew. Ker · 2014
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“Information Hiding”
Stefan Katzenbeisser and Fabien Petitcolas · 2016
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“Attention is All you Need”
Ashish Vaswani et al · 2017
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“Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints”
Jieyu Zhao et al · 2017
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“Detection of gan-generated fake images over social networks”
Francesco Marra, Diego Gragnaniello, Davide Cozzolino and Luisa Verdoliva · 2018
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“Detecting GAN-generated Imagery using Color Cues”, 2018
Scott McCloskey and Michael Albright · 2018
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“Real or fake? Learning to discriminate machine from human generated text”
Anton Bakhtin et al · 2019
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“Unmasking deepfakes with simple features”
Ricard Durall, Margret Keuper, Franz-Josef Pfreundt and Janis Keuper · 2019
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“GLTR: Statistical Detection and Visualization of Generated Text”
Sebastian Gehrmann, Hendrik Strobelt and Alexander Rush · 2019
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“RoBERTa: A robustly optimized BERT pretraining approach”
Yinhan Liu et al · 2019
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“Incremental learning for the detection and classification of GAN-generated images”
Francesco Marra, Cristiano Saltori, Giulia Boato and Luisa Verdoliva · 2019
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“Detecting GAN generated fake images using co-occurrence matrices”
Lakshmanan Nataraj et al · 2019
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“GPT-2: 1.5B release”, 2019
OpenAI · 2019
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“Release strategies and the social impacts of language models”
Irene Solaiman et al · 2019
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“Deepfake bot submissions to federal public comment websites cannot be distinguished from human submissions”
Max Weiss · 2019
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“Detecting and simulating artifacts in gan fake images”
Xu Zhang, Svebor Karaman and Shih-Fu Chang · 2019
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“Generating sentiment-preserving fake online reviews using neural language models and their human-and machine-based detection”
David Adelani et al · 2020
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“Extracting Training Data from Large Language Models”
Nicholas Carlini et al · 2020
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“Leveraging frequency analysis for deep fake image recognition”
Joel Frank et al · 2020
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“Artificial Intelligence, Values, and Alignment”
Iason Gabriel · 2020
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“RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models”
Samuel Gehman et al · 2020
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“Deepfake detection by analyzing convolutional traces”
Luca Guarnera, Oliver Giudice and Sebastiano Battiato · 2020
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“Automatic Detection of Generated Text is Easiest when Humans are Fooled”
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch and Douglas Eck · 2020
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“Automatic Detection of Machine Generated Text: A Critical Survey”
Ganesh Jawahar, Muhammad Abdul-Mageed and Laks Lakshmanan V.S · 2020
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“Global texture enhancement for fake face detection in the wild”
Zhengzhe Liu, Xiaojuan Qi and Philip Torr · 2020
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“Learning to summarize with human feedback”
Nisan Stiennon et al · 2020
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“CNN-generated images are surprisingly easy to spot … \,\ldots\, for now”
Sheng-Yu Wang et al · 2020
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“BERTScore: Evaluating Text Generation with BERT”
Tianyi Zhang et al · 2020
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“DARPA Announces Research Teams Selected to Semantic Forensics Program”, 2021
DARPA Public Affairs · 2021
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“TweepFake: About detecting deepfake tweets”
Tiziano Fagni et al · 2021
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“The Importance of Modeling Social Factors of Language: Theory and Practice”
Dirk Hovy and Diyi Yang · 2021
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“Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets”
Irene Solaiman and Christy Dennison · 2021
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“Challenges in Detoxifying Language Models”
Johannes Welbl et al · 2021
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“Recipes for Safety in Open-domain Chatbots”, 2021
Jing Xu et al · 2021
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“Constitutional AI: Harmlessness from AI Feedback”, 2022
Yuntao Bai et al · 2022
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“Poisoning and Backdooring Contrastive Learning”
Nicholas Carlini and Andreas Terzis · 2022
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“How the EU can take on the challenge posed by general-purposeAI systems”, 2022
Maximilian Gahntz and Claire Pershan · 2022
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“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, 2022
Deep Ganguli et al · 2022
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“TRUE: Re-evaluating Factual Consistency Evaluation”
Or Honovich et al · 2022
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“Temporary policy: Generative AI (e.g., ChatGPT) is banned”, 2022
Makyen · 2022
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Jacob Menick et al · 2022
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“Robust Distortion-free Watermarks for Language Models”, 2023
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto and Percy Liang · 2023
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“PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators”
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“Introducing Llama 2”, 2023
Meta · 2023
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OpenAI · 2022
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Long Ouyang et al · 2022
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“Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions”
Hammond Pearce et al · 2022
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“Red Teaming Language Models with Language Models”
Ethan Perez et al · 2022
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“Emergent Abilities of Large Language Models”
Jason Wei et al · 2022
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“Authors Guild letter seeks compensation from AI companies for using authors’ writings
*** · 2023
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“Stable Diffusion”, 2023
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“DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models”, 2023
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“The Curse of Recursion: Training on Generated Data Makes Models Forget”, 2023
Ilia Shumailov et al · 2023
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“Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence”, 2023
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“Dual-use technology—Wikipedia, The Free Encyclopedia”, 2023
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“Large language model—Wikipedia, The Free Encyclopedia”, 2023
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