arXiv:2606.00016v1 Announce Type: new Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals. We propose \textsc{AEyeDE}, an attribution-driven approach to human-AI authorship detection that leverages model attention as a discriminative signal. Specifically, we extract attention-based attribution matrices for both human- and AI-generated text using a \emph{proxy} Transformer model with white-box access and train a lightweight Convolution
As AI models achieve near human-level fluency, new and sophisticated methods are required to reliably distinguish AI-generated content from human-authored text.
The ability to accurately detect AI-generated text is critical for maintaining trust in information, intellectual property protection, and preventing misuse of generative AI.
This new attribution framework moves beyond surface statistics, enabling more robust and harder-to-evade detection of AI-generated content through deeper model analysis.
- · Content verification platforms
- · Cybersecurity firms
- · Academic researchers in AI ethics
- · Malicious actors using generative AI
- · Simple AI text detectors
- · Platforms relying on basic content moderation
Improved detection capabilities will slow the spread of sophisticated AI-generated misinformation.
The development of AEyeDE could lead to a cat-and-mouse game where AI models are trained to evade such detectors.
Increased trust in digital information, potentially reducing the 'generative AI's uncontrolled impact' narrative.
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Read at arXiv cs.CL