Semantic communication technology conserves spectrum resources, while unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surfaces (RIS), a key component of the low-altitude intelligent networks, enhance communication flexibility. Combining these technologies improves wireless performance. However, due to the openness of wireless channels, high-quality communication links are vulnerable to eavesdropping, which compromises system security. Additionally, maritime communication faces challenges such as dynamic channel variations, and high-altitude UAVs struggle to locate sea surface users and eavesdroppers. To address these issues, we propose a secure UAV-RIS-assisted communication scheme that integrates semantic communication, and integrated sensing and communications (ISAC). This scheme maximizes the secrecy semantic rate, reduces communication and computation energy consumption, and ensures constraints on sensing spectral efficiency and semantic accuracy. We jointly optimize UAV-RIS trajectories, RIS phase shifts, spectrum allocation, and the average number of semantic symbols to enhance security under eavesdropping attacks. We propose an approach that integrates semantic communication, the multi-agent softmax deep double deterministic policy gradient, and the multi-agent dueling deep Q-network (S-MA-SD5), effectively supports UAV-RIS-assisted communication in maritime environments. Performance evaluations reveal that the proposed method exceeds existing methods, achieving higher security semantic rates and lower energy consumption, thereby significantly enhancing security and efficiency in the low-altitude intelligent maritime networks.

Semantic communication optimizes spectrum usage and enhances efficiency. Integrated with uncrewed aerial vehicle (UAV) communication—a core of the low-altitude economy—it boosts data transmission in the power Internet of Things (IoT), a key part of the industrial IoT. However, power IoT wireless transmissions face security and efficiency challenges due to eavesdropping and obstructions. To address this, we propose a secure scheme integrating semantic communication and UAV relays to maximize the secrecy semantic rate while minimizing delay, energy consumption, and ensuring semantic accuracy. By jointly optimizing UAV trajectories, task local computation ratios, and channel selection, the scheme mitigates eavesdropping under cochannel interference. To tackle nonconvexity and dynamic environments, we adopt a deep reinforcement learning-based approach with semantic communication, enabling efficient UAV cooperation and resource allocation. Simulations show the proposed method improves security and energy efficiency in power IoT systems, highlighting the potential of low-altitude intelligent network.

Semantic communication technology, which transmits only relevant semantic information, can significantly conserve communication resources and reduce service time. This technology is particularly promising for unpilotedaerial vehicle (UAV)-assisted mobile edge computing (MEC) in the internet of vehicles (IoV). However, integrating semantic communication with UAV-assisted vehicle MEC is susceptible to malicious jamming. This paper introduces a reliable communication method that combines multi-modal semantic communication with UAV-assisted vehicle MEC to minimize delays in communication and computation while maintaining semantic accuracy during jamming attacks. Our approach optimizes UAV trajectories, user associations, and channel selections, enabling the UAV to select optimal positions when associating with different modal users and reducing the impact of jammers during multi-modal task reception. Due to the non-convex nature of the optimization problem and the highly dynamic environment, we employ the semantic communication combined with the multi-agent twin delayed deep deterministic policy gradient (SC-MA-TD3) approach, a multi-agent deep reinforcement learning (DRL) strategy that fosters UAV cooperation for efficient resource allocation. Simulation results show that our approach outperforms existing approaches in reducing delays and enhancing semantic accuracy.

In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

The integration of semantic communication with mobile edge computing (MEC) has emerged as a prominent research area. In this paper, we explore a novel scenario where semantic communication is integrated with unmanned aerial vehicles (UAVs) to enhance MEC, particularly in the face of jamming attacks. Our research focuses on addressing the resource management challenge to minimize task completion time and maximize semantic spectral efficiency (SSE) while adhering to quality of service requirements and resource constraints. Given the non-convexity of this problem and the dynamic behavior of jamming attacks, this paper proposes a deep reinforcement learning (DRL) algorithm by jointly optimizing UAV trajectories, user associations, and channel selections against jamming. In detail, the proposed anti-jamming DRL-based resource management approach can effectively capture the jammer’s behavior, and learn to adjust semantic task and resource scheduling strategies with the objective to minimize the negative effect of jamming attacks on task offloading and semantic communication. Simulation results demonstrate that the proposed approach outperforms baseline algorithms in terms of task completion time and total SSE under different real-world settings.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

Abstract In this paper, an image hashing scheme combining 3D space contour (TDSC) features with vector angle (VA) features is proposed. The proposed algorithm extracts the 3D contours of the local component variation features of the image and the expression changes of the local component of the image in the form of a 3D VA to improve the performance. First, the gray component of the color image is used to construct a 3D space and the contour change features of the local component of the gray image are extracted using multi-perspectives. Then, the opposite color component and the brightness component Y of the YCbCr color space are extracted from the input image. The angular features of several image components are, respectively, extracted in the 3D space. Finally, the TDSC features are combined with the VA features to obtain image hashing. The simulations demonstrate and validate that the proposed image hashing scheme not only has better classification performance compared with the other image hashing techniques but is also equipped with the performance of tamper localization.

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.