A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection

Deep neural networks (DNNs) are highly susceptible to adversarial examples---small, malicious perturbations that can cause incorrect predictions. We introduce a lightweight, plug-in detector that uses internal layer-wise inconsistencies within the target model and requires only benign data for fitti