Biblio

While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network.

For sketch-based image retrieval (SBIR), we propose a generative adversarial network trained on a large number of sketches and their corresponding real images. To imitate human search process, we attempt to match candidate images with theimaginary image in user single s mind instead of the sketch query, i.e., not only the shape information of sketches but their possible content information are considered in SBIR. Specifically, a conditional generative adversarial network (cGAN) is employed to enrich the content information of sketches and recover the imaginary images, and two VGG-based encoders, which work on real and imaginary images respectively, are used to constrain their perceptual consistency from the view of feature representations. During SBIR, we first generate an imaginary image from a given sketch via cGAN, and then take the output of the learned encoder for imaginary images as the feature of the query sketch. Finally, we build an interactive SBIR system that shows encouraging performance.