Frequency hopping multiple access (FHMA) techniques applied to critical wireless applications often encounter the effects of multiple-access interference (MAI) and follower jamming. The generalized orthogonal FH, which includes no-hit-zone (NHZ) and low-hit-zone (LHZ) FH sequence (FHS), has emerged as a highly promising hopping pattern in quasi-synchronous FHMA systems due to its ability to minimize MAI, but is unable to resist follower jamming attacks. In this paper, we firstly introduce the wide-gap property into an improved NHZ-FHS (i.e., WG-NHZ-FHS), where the frequencies at adjacent time-slots are hopped across a relatively wide gap of bandwidth, so as to address the issues of the MAI and the follower jamming. A general method to generate optimal WG-NHZ-FHS sets satisfying the theoretical bound is proposed. Subsequently, to present the merits of such a hopping pattern, the numerical error-rate performance of the WG-NHZ-FHS set imposed in a quasi-synchronous FHMA system under a follower jamming attacker is derived by using the characteristic function method; in addition, the obtained numerical result is extended to the case of WG-LHZ-FHS as the comparison benchmark. Finally, the numerical analysis and the system simulation results reveal that, compared to the previous types of FHSs, the proposed WG-NHZ-FHS set provides the lower error-rate and higher reliability for critical FHMA applications thanks to its inherent advantages of the MAI-free and the follower jamming suppression.

In this article, we introduce a novel graph representation learning architecture for spatial image steganalysis, which was motivated by the reasonable assumption that steganographic modifications will inevitably distort the statistical characteristics of the latent graph features determined from the cover images. In the architecture, we translate each image to a graph, in which the nodes represent the patches of the image and the edges between nodes indicate the local relationships between the corresponding patches. Each graph node is then associated with a feature vector determined from the corresponding patch by a shallow convolutional neural network (CNN). By feeding the graph containing node features to an attention network, the discriminative features can be learned for efficient spatial steganalysis. The experiments indicate that the reported architecture in this paper achieves a competitive detection performance compared to the benchmark CNN, which demonstrates the potential of graph neural network for steganalysis and may inspire us to develop advanced works.

In recent decades, as a green industry, electric vehicles (EVs) has a broader development perspective. Vehicle-Smart Grid ecosystem (V2G) intelligently and efficiently configures the electric energy between the electric vehicle and the grid, which can not only improve the efficiency of resource allocation, but also bring economic benefits to every entity in the system. Although the smart grid technology has become increasingly mature, security and privacy issues should not be ignored. To achieve these, a series of privacy-preserving authentication schemes have been designed for the V2G system. However, there are still some security problems like the problem of user anonymity. In order to address these issues, this work proposes a lightweight privacy-preserving authentication scheme using physical unclonable function (PUF). The proposed scheme can resist various known attacks such as impersonation attack and replay attack. Compared with previous related schemes in V2G system, our scheme has obvious advantages in terms of computing and communication overhead.

Abstract Data hiding aims to embed a secret message into a digital object such as image by slightly modifying the object content without arousing noticeable artefacts. The resultant object containing hidden information will be sent to a desired receiver via some insecure channels, e.g. images transmitted through noisy channel, social networks are vulnerable to unknown pollution or compression by a third party, which may lead the transmitted objects to be attacked such that the reconstructed message has a significant error rate. It therefore requires us to use robust embedding strategies for data hiding to realise reliable message retrieval. To this end, in this paper, a metric model to estimate the robustness of data hiding for noisy transmission based on the statistical characteristics of cover and embedding operation is presented, the former is mainly reflected by spatial frequency and texture feature, and the latter embedding operation is mainly reflected by embedding modification. The goal is to ensure that both statistical characteristics and embedding operation can be used to maximise the embedding robustness. To the best knowledge, it is the first time to estimate robustness before data hiding by a special metric model. Experimental results show that, by combining the proposed metric model in three classical data hiding methods, i.e. BPS, DE and QIM, the robustness can be significantly improved, which demonstrates its superiority and applicability.