Catch Me If You GPT : Tutorial on Deepfake Texts (2024)

Abstract: In recent years, Natural Language Generation (NLG) techniques have greatly advanced, especially in the realm of Large Language Models (LLMs). With respect to the quality of generated texts, it is no longer trivial to tell the difference between human-written and LLMgenerated texts (i.e., deepfake texts). While this is a celebratory feat for NLG, it poses new security risks (e.g., the generation of misinformation). To combat this novel challenge, researchers have developed diverse techniques to detect deepfake 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 generated texts. To understand such inter-play, two new computational problems emerge: (1) Deepfake Text Attribution (DTA) and (2) Deepfake Text Obfuscation (DTO) problems, where the DTA problem is concerned with attributing the authorship of a given text to one of k NLG methods, while the DTO problem is to evade the authorship of a given text by modifying parts of the text. In this cutting-edge tutorial, therefore, we call attention to the serious security risk both emerging problems pose and give a comprehensive review of recent literature on the detection and obfuscation of deepfake text authorships. Our tutorial will be 3 hours long with a mix of lecture and hands-on examples for interactive audience participation. You can find our tutorial materials here: https://tinyurl.com/naacl24-tutorial.

## introduction since the advent of the transformer network architecture @xcite in 2018, the field of nlg has exponentially expanded. this architectural design led to the development of @xcite , the first installment in deepfake text generative models that are capable of generating long-coherent texts. since then there have been several (i.e., @xcite , flan-t5 @xcite , llama (meta, 2023), etc). in fact, with each new installment in the world of long-coherent text generation, these generated texts become more and more human-like. such llm-generated texts are referred to as deepfake texts. while this is a great feat for the field of nlg and has several impactful applications, such text generators pose a security risk. this security risk is the potential inability to distinguish humanwritten texts from deepfake texts, which allows for malicious users of such nlg models to generate misinformation @xcite , and propaganda @xcite . therefore, we have 2 problems to tackle -(1) distinguish deepfake texts from human-written texts, and (2) detect obfuscated (i.e., a technique to evade detection) deepfake texts. while several researchers are working on these two problems, a few issues with deepfake text generation have been highlighted by other researchers. these issues or limitations include: (1) memorization & plagiarizing of training set @xcite @xcite , (2) generation of toxic & harmful speech @xcite @xcite , (3) generation of hallucinated text @xcite , (4) generation of misinformation @xcite @xcite , etc. such limitations of deepfake text generators, further confirm the need to reliably distinguish humanwritten and deepfake texts. thus, in this tutorial, we explore the following: (1) deepfake text attribution (dta) which involves correctly attributing the authorship of a given text to one of k nlg methods, and (2) deepfake text obfuscation (dto) that focus on evading the authorship of a given text by modifying parts of the text. ## target audience & prerequisites the target audience includes graduate students, practitioners, and researchers attending the naacl conference, coming from different areas of the machine learning (ml)/natural language processing (nlp)/computational linguistics (cl) field. basic common knowledge in nlp and ml would be helpful but not required. we plan to make the tutorial as self-contained as possible for a wider audience. we expect about 50-70 participants to attend our tutorial. lastly, we believe that this tutorial will be most suited to the naacl 2024 conference. ## tutorial type hence, we propose a cutting-edge tutorial with hands-on examples that will present the current research on deepfake text detection. our tutorial will be mainly a mix of lecture and hands-on style. it will include examples of the generation, detection, and obfuscation of deepfake texts for interactive participation from the audience. ## tutorial outline the materials of our tutorial will mainly contain lecture-based slideshows of this cutting-edge niche field. although we are delivering the tutorial in lecture style, we also include a few quick interactive activities to showcase real-life examples of deepfake texts and their implications. they will be polls, binary/multiple-choice, and group-based questions. ## tutors presenters of this tutorial include a diverse group of researchers. see below for their brief biographies. however, since the focus of our tutorial is on mitigation, we believe that the benefits of this tutorial outweigh the risks. additionally, our tutorial will also include strategies, like watermarking that can be used by creators of llms to further mitigate the potential negative exploitation of llms.