Adversarial Attack Bypass by Stochastic Computing

Deep learning excels by utilizing vast datasets and sophisticated training algorithms. It achieves superior performance across many machine learning challenges compared to traditional methods. However, deep neural networks (DNNs) are not flawless; they are particularly susceptible to adversarial samples during the inference phase. These inputs area deliberately designed by attackers to cause DNNs to make incorrect classifications, exploiting the networks’ vulnerabilities. This letter proposes a novel perspective to fortify the neural network (NN) defense against adversarial attacks. We enhance the NN security by employing an emerging model of computation, namely, stochastic computing (SC). We show that strengthening NN with SC counteracts the adverse effects of these attacks on an NN output and adds a vital defense layer. Our evaluation results reveal that SC notably increases NN robustness and decreases susceptibility to interference, creating secure, reliable NN systems. The proposed method improves accuracy and reduces hardware footprint and energy consumption by up to 85%, 88%, and 95%, respectively.