Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
This addresses the reliability and security of energy-efficient DNN accelerators for AI applications, showing that approximate computing is not a reliable defense, which is an incremental finding.
The paper investigates whether approximate computing universally defends against adversarial attacks in deep neural networks, finding that it can cause up to 53% accuracy loss in approximate DNNs compared to minimal loss in accurate ones, thus not being a universal defense.
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.