Kavita Kumari (Technical University of Darmstadt, Germany), Alessandro Pegoraro (Technical University of Darmstadt), Hossein Fereidooni (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fraudulent activities. Consequently, distinguishing between AI-generated and human-generated content has emerged as an intriguing research topic. However, current text detection methods lack precision and are often restricted to specific tasks or domains, making them inadequate for identifying content generated by ChatGPT. In this paper, we propose an effective ChatGPT detector named DEMASQ, which accurately identifies ChatGPT-generated content. Our method addresses two critical factors: (i) the distinct biases in text composition observed in human and machine-generated content and (ii) the alterations made by humans to evade previous detection methods. DEMASQ is an energy-based detection model that incorporates novel aspects, such as (i) optimization inspired by the Doppler effect to capture the interdependence between input text embeddings and output labels, and (ii) the use of explainable AI techniques to generate diverse perturbations. To evaluate our detector, we create a benchmark dataset comprising a mixture of prompts from both ChatGPT and humans, encompassing domains such as medical, open Q&A, finance, wiki, and Reddit. Our evaluation demonstrates that DEMASQ achieves high accuracy in identifying content generated by ChatGPT.

View More Papers

WIP: Modeling and Detecting Falsified Vehicle Trajectories Under Data...

Jun Ying, Yiheng Feng (Purdue University), Qi Alfred Chen (University of California, Irvine), Z. Morley Mao (University of Michigan and Google)

Read More

Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

Read More

Scrappy: SeCure Rate Assuring Protocol with PrivacY

Kosei Akama (Keio University), Yoshimichi Nakatsuka (ETH Zurich), Masaaki Sato (Tokai University), Keisuke Uehara (Keio University)

Read More

Large Language Model guided Protocol Fuzzing

Ruijie Meng (National University of Singapore, Singapore), Martin Mirchev (National University of Singapore), Marcel Böhme (MPI-SP, Germany and Monash University, Australia), Abhik Roychoudhury (National University of Singapore)

Read More