AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
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 Convolutional Neural Network to learn representations from these attribution maps. Across encoder-decoder translation settings, our method consistently outperforms a text-only baseline. In decoder-only settings, it performs strongly in generator-specific detection, remains competitive on standard benchmarks, and shows robustness under cross-dataset transfer and alternative-spelling perturbations. We further show that attention maps exhibit recurring local structures whose relative frequencies differ consistently between human- and AI-generated text across datasets and proxy models. These findings suggest that attention-based attribution maps provide a complementary and interpretable signal for AI-generated text detection. We will make the code publicly available to support future research.
[2606.00016] AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
[Submitted on 13 Apr 2026]
Title:AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
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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 Convolutional Neural Network to learn representations from these attribution maps. Across encoder-decoder translation settings, our method consistently outperforms a text-only baseline. In decoder-only settings, it performs strongly in generator-specific detection, remains competitive on standard benchmarks, and shows robustness under cross-dataset transfer and alternative-spelling perturbations. We further show that attention maps exhibit recurring local structures whose relative frequencies differ consistently between human- and AI-generated text across datasets and proxy models. These findings suggest that attention-based attribution maps provide a complementary and interpretable signal for AI-generated text detection. We will make the code publicly available to support future research.
Comments: 24 pages, 2 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
ACM classes: I.2.7
Cite as: arXiv:2606.00016 [cs.CL]
(or arXiv:2606.00016v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.00016
arXiv-issued DOI via DataCite
Submission history
From: Adelaide Danilov [view email] [v1] Mon, 13 Apr 2026 19:30:40 UTC (2,228 KB)
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