  Approximate computing is known for its effectiveness in improvising the
energy efficiency of deep neural network (DNN) accelerators at the cost of
slight accuracy loss. Very recently, the inexact nature of approximate
components, such as approximate multipliers have also been reported successful
in defending adversarial attacks on DNNs models. Since the approximation errors
traverse through the DNN layers as masked or unmasked, this raises a key
research question-can approximate computing always offer a defense against
adversarial attacks in DNNs, i.e., are they universally defensive? Towards
this, we present an extensive adversarial robustness analysis of different
approximate DNN accelerators (AxDNNs) using the state-of-the-art approximate
multipliers. In particular, we evaluate the impact of ten adversarial attacks
on different AxDNNs using the MNIST and CIFAR-10 datasets. Our results
demonstrate that adversarial attacks on AxDNNs can cause 53% accuracy loss
whereas the same attack may lead to almost no accuracy loss (as low as 0.06%)
in the accurate DNN. Thus, approximate computing cannot be referred to as a
universal defense strategy against adversarial attacks.
