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 l2, Deepfool, and I-FSGM, can be better eliminated by the method and provide lower latency.
Highlights
In recent years, under the background of the continuous expansion of data scale and the great improvement of computing power, artificial intelligence, and Internet of things (IoT) technology has developed rapidly
1.1 Our contributions and impact In this paper, we propose a new technique capable of effectively mitigating adversarial examples and prior knowledge about potential attacks is hardly required and consider the real situation of self-driving in 5G environment, the problem of adversarial attacks on object recognition can be effectively solved through singular value decomposition and 5G network, the process is as follows
It should be noted that Jacobian-based saliency map attack (JSMA) is perceptible perturbations, which limits the number of altered pixels but not the amplitude of the pixels
Summary
Under the background of the continuous expansion of data scale and the great improvement of computing power, artificial intelligence, and IoT technology has developed rapidly. Deep learning has achieved far better performance than others in the fields of computer vision, speech recognition, and natural language processing which make humans want to integrate deep learning technology into the IoT equipment to make them capable of making decisions especially image classification and target tracking. Its security problems are constantly exposed in the rapid development, few people pay attention about that. The adversary adds carefully designed perturbation to the image to generate adversarial examples. Are faced with a serious security threat; up to now, adversarial defense is still a great obstacle to the popularization of artificial intelligence in the field of reliability The result is the applications of driverless vehicles etc. are faced with a serious security threat; up to now, adversarial defense is still a great obstacle to the popularization of artificial intelligence in the field of reliability
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More From: EURASIP Journal on Wireless Communications and Networking
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