Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
Quick Answer
The Triospect Detection Framework enhances AI-generated text detection by incorporating content and expression perspectives, achieving significant improvements in robustness against 17 attack types.
Quick Take
The Triospect Detection Framework enhances AI-generated text detection by incorporating content and expression perspectives, achieving significant improvements in robustness against 17 attack types. It outperformed strong baselines by 22.3% (AUROC) and 13% (TPR01) on the Humanize-16K dataset, and 9.1% (AUROC) and 22% (TPR01) on the adversarial RAID. This framework sets a new standard for statistical detection methods.
Key Points
- Triospect incorporates content and expression perspectives for robust detection.
- Achieved 22.3% AUROC improvement on Humanize-16K after-attack subset.
- Demonstrated 9.1% AUROC improvement on the adversarial RAID dataset.
- Evaluated against 17 attack types across 12 domains and 17 source models.
- Data and code available at https://github.com/baoguangsheng/triospect.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 31074v1 Announce Type: new Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using additional perspectives of content (core ideas) and expression (stylistic elements) within a given text. Experiments on two benchmarks involving 17 attacks, 12 domains, and 17 source models demonstrate that Triospect is robust against these attacks.
It improves the strong baseline by a significant margin of 22. 3% (AUROC) and 13% (TPR01) on the Humanize-16K after-attack subset, and by 9. 1% (AUROC) and 22% (TPR01) on the adversarial RAID. This framework marks a pioneering effort in statistical methods to enhance detection reliability against attacks. We release our data and code at https://github. com/baoguangsheng/triospect.
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