ABSTRACT With the explosive growth in the volume of image usage, selective image encryption (SIE) has emerged as an efficient method to enhance encryption efficiency. The challenge of how to identify images containing sensitive content has long been a difficult issue. Deep learning technology, with its powerful semantic extraction capabilities, has naturally become an auxiliary tool for recognizing images containing specific content. This review primarily focuses on recent advancements in SIE integrated with semantic understanding. First, it reviews the current state of development in image ROI encryption. Subsequently, it proposes two semantic‐aware SIE schemes based on image‐to‐image and text‐to‐image search paradigms. The review also introduces evaluation metrics for assessing both the encryption algorithms and deep learning models involved in such SIE systems. Finally, it analyzes potential security issues in SIE, such as privacy protection of deep learning models and leakage of ROI edge regions, as well as possible optimization directions, including model lightweighting and encryption parallelization to enhance efficiency. In conclusion, this review indicates that selective encryption is not limited to ROI‐based approaches but also includes semantic retrieval followed by targeted encryption. Moreover, with the integration of deep learning models, considerations regarding security and efficiency have become more complex, representing key areas for further exploration in future research.

Vision-based localization plays a critical role in ensuring the positioning continuity of vehicles when Global Navigation Satellite System (GNSS) signals are unavailable. Although visual localization provides an effective auxiliary solution under GNSS-denied conditions, its performance is often constrained by insufficient landmarks. To address these limitations, collaborative vision-based localization via vehicle-to-vehicle (V2V) communication has been introduced, enabling vehicles to exchange positioning information and mitigate localization failures caused by landmark scarcity at individual nodes. However, existing studies predominantly emphasize algorithmic design, while a unified probabilistic framework for systematic performance analysis remains largely unexplored. To bridge this gap, this paper develops a novel analytical framework for collaborative vision-based localization in vehicular networks based on stochastic geometry. Specifically, the environmental landmark distribution is modeled using a homogeneous Poisson Point Process (HPPP), while vehicle locations are characterized by a Poisson Line Cox Process (PLCP). On this basis, we first derive the successful localization probability of a single vehicle relying solely on vision in GNSS-denied conditions. We then analyze the coverage probability of V2V transmissions under a Nakagami-$m$fading channel. Leveraging the derived coverage probability, a closed-form expression for the time-of-arrival (TOA)-based multi-vehicle collaborative localization probability is obtained. Finally, we define and characterize the overall GNSS-denied localization probability, which serves as a unified system-level metric quantifying the likelihood that an arbitrary vehicle can be successfully localized without GNSS support. The proposed framework explicitly reveals the coupled impacts of environmental uncertainty, wireless channel fading, and vehicular spatial distribution, thereby providing a theoretical benchmark for performance evaluation and parameter optimization of collaborative vision-based localization in vehicular networks.

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the “modulating” zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions.

This paper studies the opportunistic spectrum access (OSA) of secondary users in large-scale overlay cognitive radio (CR) networks. Particularly, a two-phase carrier sensing based protocol, namely the primary receiver assisted carrier sensing (PRA-CS) protocol, is investigated. Under the PRA-CS protocol, a secondary transmitter (ST) is allowed to transmit only if it satisfies the interference constraint at all the active primary receivers (PRs) and has the minimum back-off timer among its secondary contenders. It is worth noting that under the PRA-CS protocol, due to the fact that the activation of STs relies on the spatial realizations of both the primary and secondary networks, even the first order moment measure (average density) of the point process formed by the active STs can not be exactly characterized. To tackle this new difficulty, approximations are made on the conditional distributions of the eligible STs as well as the active STs given a typical primary/secondary receiver (PR/SR) activated at the origin. Based on such approximations, the coverage (transmission non-outage) performance of the primary/secondary network under the proposed PRA-CS protocol is characterized. Simulations are provided to validate our analysis.

This paper studies the opportunistic spectrum access (OSA) of the secondary users in a large-scale overlay cognitive radio (CR) network. Two threshold-based OSA schemes, namely the primary receiver assisted (PRA) protocol and the primary transmitter assisted (PTA) protocol, are investigated. Under the PRA/PTA protocols, a secondary transmitter (ST) is allowed to access the spectrum only when the maximum signal power of the received beacons/pilots sent from the active primary receivers/transmitters (PRs/PTs) is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocols, the concept of spatial opportunity, which is defined as the probability that an arbitrary location in the primary network is detected as a spatial spectrum hole, is introduced and then evaluated by applying tools from stochastic geometry. Based on spatial opportunity, the coverage (non-outage transmission) performance in the overlay CR network is analyzed. With the obtained results of spatial opportunity and coverage probability, we finally characterize the spatial throughput, which is defined as the average spatial density of successful transmissions in the primary/secondary network, under the PRA and PTA protocols, respectively.

This paper studies the opportunistic spectrum access (OSA) of secondary users in a large-scale overlay cognitive radio network. Particularly, a threshold-based protocol is investigated, where the secondary transmitter is allowed to access the spectrum only if the maximum signal power of the received beacons transmitted by the primary receivers is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocol, the concept of spatial opportunity is introduced and evaluated by applying tools from stochastic geometry. Due to the dependency between the realizations of the active primary and secondary users, an exact calculation of the coverage probabilities of the primary and secondary networks is infeasible. To tackle this difficulty, approximation is made on the conditional distribution of the active secondary transmitters given a typical primary/secondary receiver activated at the origin. Based on this approximation, the coverage performance of the primary/secondary network under the proposed OSA protocol is characterized. To our best knowledge, this paper is the first attempt of using stochastic geometry to evaluate the performance of the threshold-based opportunistic spectrum access in large-scale cognitive radio networks.