Adversarial Attack Mitigation Approaches Using RRAM-Neuromorphic Architectures

The rising trend and advancements in machine learning has resulted into its numerous applications in the field of computer vision, pattern recognition to providing security to hardware devices. Eventhough the proven achievements showcased by advancement in machine learning, one can exploit the vulnerabilities in those techniques by feeding adversaries. Adversarial samples are generated by well crafting and adding perturbations to the normal input samples. There exists majority of the software based adversarial attacks and defenses. In this paper, we demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. We also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.