Highly Available Cross-Domain Machine-Generated Text Detection Method

Artificial intelligence generated content (AIGC) has seriously affected information authenticity and reliability, leading to various technical and social problems such as data pollution, property ownership, and credibility crisis. Existing machine-generated text detection methods are primarily designed for specific domains and suffer from relatively low detection accuracy, making them even less effective when applied to cross-domain data such as sensitive, private, or small-sample data. To address this problem, a high available cross-domain machine-generated text detection method was proposed. This method first selected the class-center samples in any domain to train a domain-specific encoder, thereby leveraging domain features enhance boundary distinguishability. Then, an orthogonal loss function was constructed to train a domain-general encoder with the domain-specific encoder, reinforcing the general-feature of machine-generated text to support the detection across multiple domains. Experimental results on real-world data show that the detection model trained on a single domain can obtain high detection accuracy in other domains without fine-tuning, highlighting its broad applications and strong practicality.