Detecting AI-Generated Text Through Semantic Analysis
A novel framework combining transformer architectures and ensemble techniques
This research introduces a comprehensive framework for distinguishing between human and AI-generated text using advanced semantic similarity analysis techniques.
- Leverages a multi-layered architecture with DeBERTa-v3-large model, Bi-directional LSTMs, and linear attention pooling
- Captures both local and global semantic patterns unique to AI-generated content
- Enhances security capabilities for detecting increasingly sophisticated machine-generated text
- Provides a foundation for improved content authentication systems
For cybersecurity professionals, this framework offers critical tools to address growing concerns about AI-generated misinformation and content authenticity verification in an era of advanced language models.