  Advances in adversarial defenses have led to a significant improvement in the
robustness of Deep Neural Networks. However, the robust accuracy of present
state-ofthe-art defenses is far from the requirements in critical applications
such as robotics and autonomous navigation systems. Further, in practical use
cases, network prediction alone might not suffice, and assignment of a
confidence value for the prediction can prove crucial. In this work, we propose
a generic method for introducing stochasticity in the network predictions, and
utilize this for smoothing decision boundaries and rejecting low confidence
predictions, thereby boosting the robustness on accepted samples. The proposed
Feature Level Stochastic Smoothing based classification also results in a boost
in robustness without rejection over existing adversarial training methods.
Finally, we combine the proposed method with adversarial detection methods, to
achieve the benefits of both approaches.
