ReSCOPE: Learning Reconstruction-Consistent Representations for Generalizable AI-Generated Image Detection

Detectors trained on a single generator often rely on generator-specific cues that transfer poorly to unseen generators. We use consecutive reconstruction to investigate whether label-preserving reconstruction variations can provide a training signal for improving cross-generator generalization. Since consecutive reconstructions share the same fake label, their feature discrepancy reflects the encoder's sensitivity to an additional reconstruction rather than a change in real/fake labels. We use this signal to reduce reconstruction sensitivity while preserving real/fake discrimination, encouraging the encoder to learn more stable forensic cues that transfer across generators. We propose ReSCOPE, a reconstruction-consistent adaptation framework that aligns encoder representations of consecutive fake–fake reconstruction pairs while retaining source-domain real/fake supervision. Training alternates between (i) joint optimization of source-domain classification and reconstruction consistency and (ii) consistency-only adaptation. In the adapted representations, distances between consecutive reconstructions decrease while classification margins for images from unseen generators increase. Reconstruction is used only for offline training-pair construction and is not required at inference. With single-generator training, ReSCOPE achieves 97.57% mean accuracy on GenImage and 88.93% overall accuracy on Chameleon. It also improves generalization on additional curated and in-the-wild benchmarks.