Triospect combines statistical, content, and expression views to detect AI text more robustly, reporting AUROC gains of 22.3% and 9.1% on two attacked benchmarks across 17 attacks and 17 models.
Raid: A shared bench- mark for robust evaluation of machine-generated text detectors
3 Pith papers cite this work. Polarity classification is still indexing.
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Replacing characters in at least 37.5% of words with visual homoglyphs degrades authorship verification scores enough to obfuscate style, with diminishing returns past 50%.
LiSCP detects LLM-generated text via stylistic consistency profiling across paraphrased variants and reports up to 11.79% better cross-domain accuracy plus robustness to adversarial attacks.
citing papers explorer
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Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
Triospect combines statistical, content, and expression views to detect AI text more robustly, reporting AUROC gains of 22.3% and 9.1% on two attacked benchmarks across 17 attacks and 17 models.
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Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution
Replacing characters in at least 37.5% of words with visual homoglyphs degrades authorship verification scores enough to obfuscate style, with diminishing returns past 50%.
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Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation
LiSCP detects LLM-generated text via stylistic consistency profiling across paraphrased variants and reports up to 11.79% better cross-domain accuracy plus robustness to adversarial attacks.