Catch Me If You GPT: Tutorial on Deepfake Texts

Tutorial at 2023 NSF Cybersecurity Summit at Berkley, CA
Deepfake Text Tutorial Slides (draft, will be updated soon !!!)


In recent years, Neural Language Generation (NLG) techniques including large language models (LLMs) have greatly advanced, especially in the realm of open-ended text generation. With respect to the quality of generated texts, therefore, it is no longer trivial to tell the difference between human-written and NLG-generated texts (so-called “deepfake texts”). While this is a celebratory feat for NLG, however, it poses new security risks (e.g., generation of misinformation or phishing messages at scale). To combat this novel challenge, researchers have developed diverse techniques to automatically detect NLG-generated texts. While this niche field of deepfake text detection is growing, the field of NLG is growing at a much faster rate, thus making it difficult to understand the complex interplay between state-of-the-art NLG methods and the detectability of their deepfake texts. Scaling up the problem further to the case of 𝑘 NLG methods (𝑘 ≥ 2), each generating uniquely-different yet human-quality texts, two new computational problems emerge: (1) “Neural” Authorship Attribution (AA) and (2) “Neural” Authorship Obfuscation (AO) problems, where the AA problem is concerned with attributing the authorship of a given text to one of 𝑘 NLG methods, while the AO problem is to evade the authorship of a given text by modifying parts of the text. Both problems lie in the security field, and their importance and implications are growing rapidly. In this training, therefore, we call-attention to the serious security risks both emerging problems pose and give a comprehensive tutorial of recent literature on the detection and obfuscation of deepfake text authorships. Hands-on examples/quizzes of the generation, detection, and obfuscation of deepfake texts for engaging with participation interactively. We invite an audience from all sub-fields of cybersecurity to attend this very timely workshop.

Tutorial Parts

  1. Introduction
  2. Deepfake Text Detection
  3. Deepfake Text Obfuscation
  4. Conclusion & Future Work

See Similar Tutorials:

  1. Tutorial at The Web Conference 2023,
  2. Tutorial at 15th International Natural Language Generation Conference 2022 Conference,


Adaku Uchendu recently earned her Ph.D. in Information Sciences and Technology from The Pennsylvania State University and is to join MIT Lincoln Lab. She was a Sloan scholarship fellow, an NSF CyberCorps SFS scholar, and a Button-Waller fellow. Her dissertation is titled “Reverse Turing Test in the Age of Deepfake Texts.” She has authored several papers in deepfake text detection at top-tier conferences & journals - EMNLP, KDD Exploration, Web of Science, Web Conference, etc. In addition, she led two similar Tutorials titled, Tutorial on Artificial Text Detection at the INLG conference, in July 2022 and Catch Me If You GAN: Generation, Detection, and Obfuscation of Deepfake Texts at the Web conference in April 2023. She is interested in building robust and explainable deepfake text detectors to assist in both automatic and human detection of deepfake texts. More details of her research can be found at: E-mail:

Thai Le is an Assistant Professor at The University of Mississippi since 2022. Before starting at the University of Mississippi, he worked at Amazon Alexa and obtained his doctorate degree from The Pennsylvania State University. He has published several relevant works at top-tier conferences such as KDD, ICDM, ACL, EMNLP, and Web Conference. He is also one of the Instructors in the Tutorial, Catch Me If You GAN: Generation, Detection, and Obfuscation of Deepfake Texts at the Web Conference. In general, he researches the trustworthiness of machine learning and AI, with a focus on explainability and adversarial robustness of machine learning models. He also contributed to the adversarial NLP open-source repository TextAttack1, which will be used to demonstrate the authorship obfuscation section of the tutorial. More details of his research can be found at: E-mail:

Dongwon Lee is a Professor in the College of Information Sciences and Technology (a.k.a. iSchool) at Penn State University, USA and also an ACM Distinguished Scientist and Fulbright Cyber Security Scholar. Before starting at Penn State, he worked at AT&T Bell Labs and obtained his Ph.D. in Computer Science from UCLA. From 2015 to 2017, he also served as a Program Director at National Science Foundation (NSF), co-managing cybersecurity education and research programs and contributing to the development of national research priorities. In general, he researches problems in the areas of data science, machine learning, and cybersecurity. Since 2017, in particular, he has led the SysFake project at Penn State, investigating computational and socio-technical solutions to better combat fake news. More details of his research can be found at: Previously, he has given numerous tutorials at various venues, including WWW, AAAI, CIKM, SDM, ICDE, and WebSci. E-mail: