Beyond Words: An Advanced Ensemble Framework for Unmasking AI-Generated Content Through Linguistic Fingerprinting

Abstract

AI-generated content detection is vital because it helps to uphold digital integrity in most fields of application, such as in academic publishing and content verification. The process of identifying text authenticity and traceability of the source is dependent on proper detection means. The approach introduced in this paper is a novel ensemble method that combines machine learning and linguistic analysis for AI content detection. The ensemble approach uses a set of classification algorithms to identify the most important differences between human-authored and AI-generated text. To validate the proposed method, this study utilized an extensive collection of text samples (20,000) obtained from SQuAD 2.0, CNN/Daily Mail, GPT-3.5, and ChatGPT datasets. The proposed ensemble model achieved precision, accuracy, recall, and F1-score of 97.2%, 97.5%, 96.4%, and 97.3%, respectively, demonstrating superior performance compared to individual classifiers. The experimental results demonstrate that the ensemble approach offers efficient detection performance, which can be applied to various text types and lengths, and thus can be implemented in practical systems for content verification and academic integrity assessment.