  Neural networks have achieved remarkable performance in computer vision,
however they are vulnerable to adversarial examples. Adversarial examples are
inputs that have been carefully perturbed to fool classifier networks, while
appearing unchanged to humans. Based on prior works on detecting adversaries,
we propose a structured methodology of augmenting a deep neural network (DNN)
with a detector subnetwork. We use $\textit{Adversarial Noise Sensitivity}$
(ANS), a novel metric for measuring the adversarial gradient contribution of
different intermediate layers of a network. Based on the ANS value, we append a
detector to the most sensitive layer. In prior works, more complex detectors
were added to a DNN, increasing the inference computational cost of the model.
In contrast, our structured and strategic addition of a detector to a DNN
reduces the complexity of the model while making the overall network
adversarially resilient. Through comprehensive white-box and black-box
experiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves
state-of-the-art detector robustness against adversarial examples. Furthermore,
we validate the energy efficiency of our proposed adversarial detection
methodology through an extensive energy analysis on various hardware scalable
CMOS accelerator platforms. We also demonstrate the effects of quantization on
our detector-appended networks.
