Prototype Margins and Classifier Decisions: A Geometric Analysis of Deepfake Attribution Robustness

Deepfake attribution models are vulnerable to image degradations, but whether prototype geometry reliably reflects classifier decisions remains unclear. We introduce Attribution Margin Collapse (AMC), a diagnostic framework that examines class-center displacement and compares prototype margins with classifier logit margins. Two complementary statistics, AMC-S and AMC-F, summarize the mean prototype margin and the dataset-wide magnitude of negative prototype margins, respectively. Experiments on FaceForensics++ across four attribution models document robustness degradation, while JPEG analysis reveals similar aggregate trends in prototype and classifier margins. A complementary cross-domain study of binary deepfake detection on Celeb-DF reveals substantial disagreement: samples can retain positive prototype margins despite incorrect classifier predictions. These findings highlight both the utility and the limitations of prototype geometry for robustness analysis. AMC provides a framework for examining when geometric diagnostics reflect classifier behavior and when they offer an incomplete account of failure.