Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
URL https: //arxiv.org/abs/2309.02731
3 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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DetectZoo is a unified toolkit providing reference implementations of 61 detectors, native loaders for 22 benchmark datasets, and a standardized evaluation pipeline for AI-generated content detection across text, audio, and image modalities.
Feature-augmented DeBERTa-v3-base with attention-based fusion reaches 85.9% balanced accuracy on the multi-domain M4 benchmark under fixed-threshold evaluation, outperforming zero-shot baselines by up to 7.22 points.
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Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
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DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities
DetectZoo is a unified toolkit providing reference implementations of 61 detectors, native loaders for 22 benchmark datasets, and a standardized evaluation pipeline for AI-generated content detection across text, audio, and image modalities.
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Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators
Feature-augmented DeBERTa-v3-base with attention-based fusion reaches 85.9% balanced accuracy on the multi-domain M4 benchmark under fixed-threshold evaluation, outperforming zero-shot baselines by up to 7.22 points.