Affiliation:
1. Clemson University
2. Walmart (United States)
3. University of South Carolina
4. South Carolina State University
5. Benedict College
6. The Citadel
Abstract
Abstract
Deep Learning (DL)-based image classification models are essential for autonomous vehicle (AV) perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrectly classified traffic signs by the perception module of an autonomous vehicle. In this study, we create and compare Hybrid Classical-Quantum Deep Learning (HCQ-DL) models with Classical Deep Learning (C-DL) models to demonstrate robustness against adversarial attacks for perception modules. Before feeding them into the quantum system, we used transfer learning models like AlexNet and VGG-16 as feature extractors. We tested over 1000 quantum circuits in our HCQ-DL models for Projected Gradient Descent (PGD), Fast Gradient Sign Attack (FGSA), and Gradient Attack (GA), which are three well-known untargeted adversarial approaches. We evaluated the performance of all models during adversarial attack and no-attack scenarios. Our HCQ-DL models maintain accuracy above 95% during a no-attack scenario and above 91% for GA and FGSA attacks, which is higher than C-DL models. During the PGD attack, our AlexNet-based HCQ-DL model maintained an accuracy of 85% compared to C-DL models that achieved accuracies below 21%.
Publisher
Research Square Platform LLC
Reference45 articles.
1. Ren S, He K, Girshick R, Sun J, “Faster R-CNN (2017) : Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, Jun. doi: 10.1109/TPAMI.2016.2577031
2. Lin T-Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) “Feature Pyramid Networks for Object Detection,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. Accessed: Aug. 01, 2021. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2017/html/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.html
3. Liu J, An F-P (2020) “Image Classification Algorithm Based on Deep Learning-Kernel Function,” Scientific Programming, vol. p. e7607612, Jan. 2020, doi: 10.1155/2020/7607612
4. Perception, Planning, Control, and Coordination for Autonomous Vehicles;Pendleton S;Machines
5. Szegedy C et al (2014) “Intriguing properties of neural networks,” arXiv:1312.6199 [cs], Accessed: Aug. 01, 2021. [Online]. Available: http://arxiv.org/abs/1312.6199