Efficient AI-Generated Text Detection Based on Contrastively Enhanced Hybrid Features and Support Vector Machine

With the rapid development of generative large-language models, the text they generate is becoming increasingly difficult to distinguish from human-written text, creating an urgent need for efficient detection techniques. However, existing high-precision detection methods often come with significant computational overhead and inference latency. To address this challenge, this paper proposes an efficient AI-generated text detection model based on contrastive learning-enhanced hybrid features and support vector machines (SVMs). The model first constructs hybrid features by integrating deep semantic features with handcrafted features. Subsequently, a contrastive learning strategy is employed to enhance the hybrid features, and the enhanced features are used to train the SVMs.