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With the increasing deployment of deep neural networks in safety-criticalapplications such as self-driving cars, medical imaging, anomaly detection,etc., adversarial robustness has become a crucial concern in the reliability ofthese networks in real-world scenarios. A plethora of works based onadversarial training and regularization-based techniques have been proposed tomake these deep networks robust against adversarial attacks. However, thesemethods require either retraining models or training them from scratch, makingthem infeasible to defend pre-trained models when access to training data isrestricted. To address this problem, we propose a test time Data-freeAdversarial Defense (DAD) containing detection and correction frameworks.Moreover, to further improve the efficacy of the correction framework in caseswhen the detector is under-confident, we propose a soft-detection scheme(dubbed as "DAD++"). We conduct a wide range of experiments and ablations onseveral datasets and network architectures to show the efficacy of our proposedapproach. Furthermore, we demonstrate the applicability of our approach inimparting adversarial defense at test time under data-free (or data-efficient)applications/setups, such as Data-free Knowledge Distillation and Source-freeUnsupervised Domain Adaptation, as well as Semi-supervised classificationframeworks. We observe that in all the experiments and applications, our DAD++gives an impressive performance against various adversarial attacks with aminimal drop in clean accuracy. The source code is available at:https://github.com/vcl-iisc/Improved-Data-free-Test-Time-Adversarial-Defense

DAD++: Improved Data-free Test Time Adversarial Defense