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