Adversarial Examples Detection Based on Adversarial Attack Sensitivity

We propose ADAS, a detection method that exploits the sensitivity disparity between clean and adversarial samples under re-attacks. ADAS achieves strong robustness to minimal-perturbation attacks and shows good generalization to unseen adversarial methods across multiple datasets and architectures.