Full-range Binary Classifier Calibration for Stable Model Updates in Production
Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of new threats. Retraining tends to change the output prediction scores, which breaks downstream users of the model. For these security-oriented models we need consistent false-positive rate (FPR) across all output values, whereas standard probability-calibration methods target class probability rather than an FPR contract. We introduce a method built on top of existing calibration primitives that targets the whole FPR curve, giving scores a consistent FPR meaning across deplo...
| Authors | Konstantin Berlin |
|---|---|
| Primary category | cs.CR |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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