Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via Adversarial Retraining), an approach designed to fortify adversarial detectors against such adaptive attacks while preserving the classifier’s accuracy. RADAR employs adversarial training by incorporating adversarial examples—crafted to deceive both the classifier and the detector—into the training process. This dual optimization enables the detector to learn and adapt to sophisticated attack scenarios. Comprehensive experiments on CIFAR-10, SVHN, and ImageNet datasets demonstrate that RADAR substantially enhances the detector’s ability to accurately identify adaptive adversarial attacks without degrading classifier performance.