Defense against adversarial attacks in traffic sign images identification based on 5G
Fei Wu, Limin Xiao, Yang Wenxue, Jinbin Zhu
Abstract
Abstract In the past decade, artificial intelligence and Internet of things (IoT) technology have been rapid development, gradually began to integrate with each other, especially in coming 5G era. Admittedly, image recognition is the key technology due to a huge number of video cameras integrated in intelligent IoT equipment, such as driverless cars. However, the rapidly growing body of research in adversarial machine learning has demonstrated that the deep learning architectures are vulnerable to adversarial examples. Thus, the raises questions about the security of intelligent Internet of thing (IoT) and trust sensitive areas. This emphasizes the urgent need for practical defense technology that can be deployed to real-time combat attacks at any time. Well-crafted small perturbations lead to the misclassification of legitimate images by neural networks, but not the human visual system. It is worth noting that many attack strategies are designed to disrupt image pixels in a visually imperceptible manner. Therefore, we propose a new defense method and take full advantage of 5G high-speed bandwidth and mobile edge computing (MEC) effectively. We use singular value decomposition (SVD) which is the optimal approximation of matrix in the sense of square loss to eliminate the perturbation. We have conducted extensive and large-scale experiments with German Traffic Sign Recognition Benchmark (GTSRB) datasets and the results show that adversarial attacks, such as Carlini-Wagner’s l 2 , Deepfool, and I-FSGM, can be better eliminated by the method and provide lower latency.