AI-Generated Text Detection Technology: A Deepfake Text Recognition Scheme Based on Multi-Model Fusion

This article introduces an open-source AI-generated text detection project. Its core is to combine traditional machine learning (SVM, Naive Bayes) with pre-trained language models (RoBERTa) to build a multi-level recognition system, addressing the information authenticity challenges posed by deepfake text. The project covers the entire process including data preprocessing, feature engineering, and model comparison experiments, providing a reference for related research and applications.

With the popularity of large models like ChatGPT, AI-generated text has permeated various fields, making it a challenge to distinguish whether text is written by humans. This project is trained and tested on the MAGE dataset, adopting a hybrid technical route: combining traditional ML and deep learning. The core technology stack includes NLTK preprocessing, SVM/Naive Bayes, RoBERTa model, and a multi-dimensional evaluation framework.

Data Preprocessing: Use NLTK for cleaning, tokenization, standardization, etc.; Feature Engineering: Traditional ML uses BoW and TF-IDF to extract statistical features, while RoBERTa automatically learns deep semantic features; Model Architecture: Traditional ML uses linear kernel SVM and Naive Bayes, and RoBERTa is fine-tuned for binary classification training.

Evaluated using accuracy, precision, recall, and F1 on the MAGE dataset. The results show that RoBERTa outperforms traditional ML in all metrics (benefiting from context understanding), but traditional models are better in computational efficiency and interpretability, making them suitable for resource-constrained scenarios.

Application Scenarios: Content platform moderation, academic integrity detection, news verification, social media monitoring; Deployment Recommendations: Use SVM for initial screening in real-time scenarios, RoBERTa for precise detection in high-accuracy scenarios, or adopt a cascaded architecture (lightweight filtering + heavyweight precise detection).

Limitations: Vulnerable to adversarial attacks, poor generalization to unseen generative models, only supports English; Future Directions: Introduce more advanced pre-trained models, use adversarial training to improve robustness, develop cross-language universal solutions.

AI-generated text detection needs to combine NLP fundamentals with the progress of large models. This project provides a complete baseline implementation, covering the entire process from preprocessing to evaluation, offering valuable references for related research and application development.