A Novel Lightweight Defense Method Against Adversarial Patches-Based Attacks on Automated Vehicle Make and Model Recognition Systems

In smart cities, connected and automated surveillance systems play an essential role in ensuring safety and security of life, property, critical infrastructures and cyber-physical systems. The recent trend of such surveillance systems has been to embrace the use of advanced deep learning models such as convolutional neural networks for the task of detection, monitoring or tracking. In this paper, we focus on the security of an automated surveillance system that is responsible for vehicle make and model recognition (VMMR). We introduce an adversarial attack against such VMMR systems through adversarially learnt patches. We demonstrate the effectiveness of the developed adversarial patches against VMMR through experimental evaluations on a real-world vehicle surveillance dataset. The developed adversarial patches achieve reductions of up to 48 % in VMMR recall scores. In addition, we propose a lightweight defense method called SIHFR (stands for Symmetric Image-Half Flip and Replace) to eliminate the effect of adversarial patches on VMMR performance. Through experimental evaluations, we investigate the robustness of the proposed defense method under varying patch placement strategies and patch sizes. The proposed defense method adds a minimal overhead of less than 2ms per image (on average) and succeeds in enhancing VMMR performance by up to 69.28 %. It is hoped that this work shall guide future studies to develop smart city VMMR surveillance systems that are robust to cyber-physical attacks based on adversarially learnt patches.