Hardware Level Countermeasures for Adversarial Attacks in Machine Learning Devices

Abstract

The increasing deployment of machine learning (ML) systems in critical applications such as healthcare, finance, autonomous driving, and defense has drawn significant attention to their vulnerability to adversarial attacks. These attacks, which manipulate input data to induce misclassification, pose serious threats to the integrity and reliability of intelligent devices. While software-based defense strategies have been extensively studied, they are often insufficient against sophisticated adversaries, particularly in edge and embedded systems with limited resources. This paper investigates hardware-level countermeasures designed to enhance the resilience of ML devices against adversarial attacks. By leveraging circuit-level design principles, memory security, noise injection, and secure accelerators, hardware countermeasures provide robust protection that complements software defenses. Through experimental evaluations on FPGA-based ML accelerators, we demonstrate the effectiveness of hardware-based defenses in mitigating adversarial perturbations without significantly impacting computational efficiency. Our results suggest that integrating secure hardware mechanisms into ML devices provides a sustainable path toward ensuring trustworthy and resilient AI systems at scale.