Per-Sample or Not: A Granularity Criterion for Test-Time Correction in AI-Generated Image Detection

A linear head on a frozen visual foundation model provides strong AI-generated image (AIGI) detection, but its performance degrades under unseen generators, content domains, and dissemination shifts. We first establish a rank-invariance criterion showing that test-time corrections that only adjust thresholds or apply sample-shared feature translations cannot improve AUROC because they preserve the relative score ordering of real and generated samples. Motivated by the channel-wise non-uniformity and target specificity of forensic feature shifts, we propose Forensic-Shift Conditional Test-Time Affine Adaptation (FS-CTAA). Trained solely on source data with simulated forensic shifts, FS-CTAA predicts sample-specific channel-wise affine parameters from each test feature, the source training-set feature mean, and the current test-subset feature mean, requiring neither target labels nor gradient updates. Across four heterogeneous frozen backbones, FS-CTAA improves macro-average AUROC by $1.67$–$3.49$ percentage points and, averaged over three training seeds, outperforms a sample-shared modulator trained under the same protocol by $1.22$–$1.58$ percentage points, showing an additional ranking benefit from sample-specific rather than merely target-conditioned adaptation. Gains concentrate on challenging shifts where the frozen probe leaves substantial ranking errors, while performance remains stable on already near-saturated cases. FS-CTAA further improves balanced accuracy by $2.3$–$8.5$ percentage points over the frozen probe and exceeds the threshold-only target-label oracle on three backbones. These results establish sample-conditioned feature adaptation as an effective approach to robust AIGI detection under distribution shifts.