C-Net: A Compression-Based Lightweight Network for Machine-Generated Text Detection
Yinghan Zhou, Juan Wen, Jianghao Jia, Liting Gao, Ziwei Zhang
Abstract
In recent years, large language models (LLM) have progressed rapidly, leading to growing concerns about the proliferation of difficult-to-distinguish AI-generated content. This has given rise to a range of issues, including fake news, academic fraud, phishing emails, posing significant dangers across various domains. However, current machine-generated text (MGT) detection methods still face challenges, including the need to access model's output logits or losses, which makes it unable to adapt to black-box scenarios in the real world, and difficult to deploy models with large parameter sizes. Therefore, we propose a compression-based lightweight network for MGT detection that leverages the ability of lossless compression to effectively extract features between categories. With fewer parameters, our framework achieves state-of-the-art performance in MGT detection under black box conditions. Experiments demonstrate that our approach performs exceptionally well on both Chinese and English datasets. Specifically, our method achieves a fulltext detection accuracy of 99.5%, surpassing the previous SOTA method.