Adversarial Patches-based Attacks on Automated Vehicle Make and Model Recognition Systems

Abdul Jabbar Siddiqui, Azzedine Boukerche

Abstract: The recent works on automated vehicle make and model recognition (VMMR) have embraced the use of advanced deep learning models such as convolutional neural networks. In this work, we introduce an adversarial attack against such VMMR systems through adversarially learnt patches. We demonstrate the effectiveness of the adversarial patches against VMMR through experimental evaluations on a real-world surveillance dataset. The developed adversarial patches achieve reductions of upto 37% in VMMR recall scores. It is hoped that this work shall motivate future studies in developing VMMR systems that are robust to adversarial learning-based attacks.