ROAST: Risk-aware Outlier-exposure for Adversarial Selective Training of Anomaly Detectors Against Evasion Attacks
This addresses the need for robust anomaly detection in safety-critical domains like healthcare, though it is an incremental improvement focused on selective training.
The paper tackles the problem of improving anomaly detector recall against evasion attacks in healthcare DNNs by proposing ROAST, a risk-aware selective training framework that increases recall by 16.2% and reduces training time by 88.3% while maintaining precision.
Safety-critical domains like healthcare rely on deep neural networks (DNNs) for prediction, yet DNNs remain vulnerable to evasion attacks. Anomaly detectors (ADs) are widely used to protect DNNs, but conventional ADs are trained indiscriminately on benign data from all patients, overlooking physiological differences that introduce noise, degrade robustness, and reduce recall. In this paper, we propose ROAST, a novel risk-aware outlier exposure selective training framework that improves AD recall without sacrificing precision. ROAST identifies patients who are less vulnerable to attack and focuses training on these cleaner, more reliable data, thereby reducing false negatives and improving recall. To preserve precision, the framework applies outlier exposure by injecting adversarial samples into the training set of the less vulnerable patients, avoiding noisy data from others. Experiments show that ROAST increases recall by 16.2\% while reducing the training time by 88.3\% on average compared to indiscriminate training, with minimal impact on precision.