The autonomous driving industry has mushroomed over the past decade. Although autonomous driving has undoubtedly become one of the most promising technologies of this century, its development faces multiple challenges, of which security is the major concern. In this article, we present a thorough analysis of autonomous driving security. First, the attack surface of autonomous driving is presented. After an analysis of the operation of autonomous driving in terms of key components and technologies, the security of autonomous driving is elaborated in four dimensions: 1) sensors; 2) operating system; 3) control system; and 4) vehicle-to-everything (V2X) communication. Sensor security is examined from five components, which are mainly responsible for self-positioning and environmental perception. The analysis of operating system security, the second dimension, is concentrated on the robot operating system. Concerning the control system security, the controller area network is approached mainly from vulnerabilities and protection measures. The fourth dimension, V2X communication security, is probed from four categories of attacks: 1) authenticity/identification; 2) availability; 3) data integrity; and 4) confidentiality with corresponding solutions. Moreover, the drawbacks of existing methods adopted in the four dimensions are also provided. Finally, a conceptual multilayer defense framework is proposed to secure the information flow from external communication to the physical autonomous vehicle.

In the field of image enhancement, using deep learning methods to enhance low-light images is currently mainstream. However, the methods often have complex network structures of a large number of parameters, and their training often uses paired data-sets, which are difficult to obtain in actual practice. To solve these problems, this paper proposes a simple generative adversarial network and Retinex model, dubbed RetinexGAN, that is completely trained using unpaired data-sets. It contains a decomposition network and two discriminator networks. To reduce the parameters of the network, only two convolution layers are used in the decomposition network. We show more challenging testing data where some parts of the image are underexposed and others are normal light. Both quantitative and visual results show that RetinexGAN is largely superior to state-of-the-art methods.

Communication performance relies largely on the availability of channel state information (CSI). This paper investigates the impact of outdated CSI on the achievable covert communication performance in a two-hop wireless relay system under two typical covert transmission schemes of rate-control transmission (RCT) and power-control transmission (PCT). We first apply the typical channel feedback delay model to determine the statistical distribution of outdated CSI, based on which we then develop theoretical models to depict the inherent relationship between outdated CSI and the fundamental covert performance metrics in terms of detection error probability (DEP) and covert rate (CR). With the help of these models, we further explore the optimization of DEP subject to the CR constraint as well as the optimization of CR subject to the DEP constraint to reveal the max-min DEP performance and the maximal CR performance with the outdated CSI. Finally, extensive numerical results are provided to illustrate the impact of outdated CSI on the covert communication performance.