AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
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 Convolutional Neural Network to learn representations from these attribution maps. Across encoder-decoder translation settings, our method consistently outp...
| Authors | Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer et al. |
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| Primary category | cs.CL |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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