FSED: A Feature-Space Ensemble Defense for Detecting Adversarial Examples
This paper introduces a Feature-Space Ensemble Defense (FSED) framework, which involves adversarial training, joint confidence calibration, and class-conditional Mahalanobis feature-space anomaly scoring to facilitate powerful adversarial detection. The proposed method considers the final-layer uncertainty. It also mod...