Test-Time Curriculum for Open-Set AIGC Detection

• URL: http://arxiv.org/abs/2608.00559v1
• Date: Sat, 01 Aug 2026 09:42:45 GMT
• Title: Test-Time Curriculum for Open-Set AIGC Detection
• Abstract summary: Test-Time Curriculum (TTC) adapts a detector on unlabeled test data through curriculum-based self-training.<n>TTC starts from highly reliable pseudo-labeled samples and incorporates harder yet informative cases.<n>Cross-Scale Pseudo-Label Refinement aggregates complementary evidence across multiple resolutions for more reliable adaptation.
• Score: 31.702537861903014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing methods improve cross-generator generalization through better representations or training data construction, they typically follow a static train-once-and-deploy paradigm and cannot adapt after deployment. In this work, we study open-set AIGC image detection from a test-time adaptation perspective. We propose Test-Time Curriculum (TTC), a simple and model-agnostic framework that adapts a detector on unlabeled test data through curriculum-based self-training. TTC starts from highly reliable pseudo-labeled samples and progressively incorporates harder yet informative cases, while enforcing class-balanced selection to reduce biased updates under generator shift. To further improve pseudo-label quality, we introduce Cross-Scale Pseudo-Label Refinement, which aggregates complementary evidence across multiple resolutions for more reliable adaptation, and applies noisy-or fusion at inference to strengthen final predictions. In addition, we construct AIGCGuard, a new benchmark containing 3,100 representative real images and 124,000 generated images from 40 of the most advanced open-source and proprietary text-to-image models. Extensive experiments on five benchmarks show that TTC substantially improves overall detection performance under diverse unseen-generator shifts, establishing a practical and effective test-time adaptation framework for open-set generated image detection.

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