Authorship Forensics Portal

Abstract

This paper presents the research outcome, Authorship Forensics Portal, leveraging both Statistical Natural Language Processing (SNLP) and Convolutional Neural Networks (CNN) techniques to differentiate documents written by humans and ChatGPTs. The portal allows teachers to (1) upload labeled data that contains written text and its author; (2) configure parameters that are required for training models, e.g., 2-class (i.e., human and ChatGPT) or 3-class (i.e., human, ChatGPT 3.5, and ChatGPT as well as the train/test set split ratio, validation set ratio, and validation accuracy threshold for stopping the training process; (3) review the details of a trained model, e.g., the train/test set, the time spent, the prediction results like numbers, true positive, false positive, precision, recall, and f-value, etc.; (4) make their own trained models be private so only themselves can see and use or be public so other teachers can also see and use; and, (5) ask a chosen trained model for its opinion on whether a piece of text written by human or generative Al (e.g., ChatGPT for 2•class prediction and ChatGPT 3.5 or ChatGPT 4 for 3-class prediction). The results demonstrate a significant ability of the models to distinguish between human and Al-written text, with highest precision 0.9868 (Fo_5 score 0.9647) for the 2-class (human and ChatGPT) testing subset and highest precision 0.9875 (Fo.5 score 0.9753) for the 3-class (human, ChatGPT 3.5, and ChatGPT 4) testing subset.