AIDetx: a compression-based method for identification of machine-learning generated text

• URL: http://arxiv.org/abs/2411.19869v1
• Date: Fri, 29 Nov 2024 17:31:42 GMT
• Title: AIDetx: a compression-based method for identification of machine-learning generated text
• Authors: Leonardo Almeida, Pedro Rodrigues, Diogo Magalhães, Armando J. Pinho, Diogo Pratas,
• Abstract summary: This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. We evaluate AIDetx on two benchmark datasets, achieving F1 scores exceeding 97% and 99%, respectively. Compared to current methods, such as large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution.
• Score: 0.8411424745913135
• License:
• Abstract: This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs and limited interpretability. To address these limitations, we propose a compression-based classification framework that leverages finite-context models (FCMs). AIDetx constructs distinct compression models for human-written and AI-generated text, classifying new inputs based on which model achieves a higher compression ratio. We evaluated AIDetx on two benchmark datasets, achieving F1 scores exceeding 97% and 99%, respectively, highlighting its high accuracy. Compared to current methods, such as large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution, significantly reducing both training time and hardware requirements (e.g., no GPUs needed). The full implementation is publicly available at https://github.com/AIDetx/AIDetx.

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