Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Image tasks, attackers can generate a series of images containing inappropriate content by simply editing the language modality input. To mitigate this security concern, numerous guarding or defensive strategies have been proposed, with a particular emphasis on safeguarding language modality. However, in practical applications, threats in the vision modality, particularly in tasks involving the editing of real-world images, present heightened security risks as they can easily infringe upon the rights of the image owner. Therefore, this paper employs a method named typographic attack to reveal that various image generation models are also susceptible to threats within the vision modality. Furthermore, we also evaluate the defense performance of various existing methods when facing threats in the vision modality and uncover their ineffectiveness. Finally, we propose the Vision Modal Threats in Image Generation Models (VMT-IGMs) dataset, which would serve as a baseline for evaluating the vision modality vulnerability of various image generation models.Warning: This paper includes content that may cause discomfort or distress. Potentially disturbing content has been blocked and blurred.

Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the transferability of adversarial examples remains an ongoing challenge. In this paper, we specifically analyze the manifestation of adversarial transferability among MLLMs and identify the key factors that influence this characteristic. We discover that the transferability of MLLMs exists in cross-LLM scenarios with the same vision encoder and indicate two key Factors that may influence transferability. We provide two semantic-level data augmentation methods, Adding Image Patch (AIP) and Typography Augment Transferability Method (TATM), which boost the transferability of adversarial examples across MLLMs. To explore the potential impact in the real world, we utilize two tasks that can have both negative and positive societal impacts: 1. Harmful Content Insertion and 2. Information Protection.

The spatial resolution is one of the key issues to be addressed for wireless sensing, which traditionally relies on the signal bandwidth. However, directly enhancing the signal bandwidth is less effective, due to the challenges in (i) hardware implementations for large bandwidth signal generation and reception, and (ii) strict wireless spectrum management. Therefore, solutions those try to combine observations from multi-band signals to improve the spatial resolution have become attractive so far. In this paper, we propose a scattering centers cutting algorithm (SCC) based on the well-known Geometrical Theory of Diffraction (GTD) model, for frequency stitching sensing. A method based on the generalized likelihood ratio test (GLRT) method and SCC are combined, to optimize the target model order and related coefficients, which can greatly improve the sensing performance. Simulation results and real-world experiments are both provided, to show the effectiveness of the proposed SCC solution.

Wireless localization and sensing both rely on the accurate extraction of wideband signal metrics, such as the delay and Doppler shift. The integrated sensing and localization (ISAL) aims to improve the hardware and spectrum utilization by taking advantage of the similarity between wireless localization and sensing. Generally, there are two schemes to realize the localization and sensing functions in the multi-slot integrated sensing and localization network: the two-step scheme and the integrated scheme. Considering the complexity, this paper proposes a dual-slot integrated sensing and localization network model with spatiotemporal cooperation information, and gives the Fisher information matrix (FIM) exploiting all the channel state information (CSI) of the model. Moreover, the energy allocation strategy between the two slots and the power allocation strategy in each single slot are formulated and solved. The numerical results show that by applying the resource optimization strategies, the target sensing accuracy achieved by the two-step scheme is able to exceed that achieved by the integrated scheme.

In recent years, unmanned aerial vehicles (UAV) have been widely adopted to assist sensing, localization and communication for ground devices (GDs), especially for emergency applications. Since the location of GDs cannot be perfect known to UAVs in such cases, we present a novel UAV aided sensing, localization and communication integrated system. We first introduce the idea of age of information (AoI) to evaluate the timeliness of information from GDs, which is essentially determined by the time of UAV movement, localization operation and data transmission. In particular, we focus on the time-critical network scheduling policies, which could be formulated as a joint UAV trajectory, UAV three-dimensional location and bandwidth allocation optimization problem. This Non-convex problem can be decoupled into two subproblems, which could be solved by specified low complexity algorithms. Simulation results are provided, and can verify the effectiveness in timeliness of our proposed scheduling strategies.

Multi-functional wireless networks are becoming promising for many modern applications, e.g., the co-design of the waveforms or the shared spectrum between sensing and localization may achieve higher spectrum efficiency. To exploit the full potential of the integrated sensing and localization network (ISLN), we try to provide the general fundamental limits of ISLN. Moreover, the power allocation strategies are formulated and solved accordingly, to show the possible tradeoffs between two different performance metrics. Numerical results are provided to validate our analysis. We can see that, (i) when the network infrastructure is limited, cooperative measurements among agents will become important. (ii) In general cases, the sensing and localization could incorporate via proper power allocation strategies. However, (iii) when the power resources are strictly limited, there will exist an obvious tradeoff between the two tasks.

High-precision location information formulates the basis of the modern Internet of Things (IoT). However, since the navigation signals from the global navigation satellite systems (GNSSs) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required for ground devices (GDs). Due to the advantages of their flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) show significant potential in this ground localization enhancement system. In this article, we propose a UAV aided positioning (UAP) system for GDs, where the UAVs provide valuable flying Line of Sight (LoS) observations. Specifically, we first give the fundamental limits of the proposed UAP system in terms of the Cramer–Rao low bound (CRLB), where the UAVs are treated as “agents” with unknown positions instead of anchors. Then, we formulate a general UAP method using the nonparametric belief propagation (NBP)-based probabilistic framework, to jointly positioning UAVs and GDs simultaneously. Moreover, a two-step clustering-based solution is given to tackle the data association challenge in the multi-UAV scenarios. We also show that proper data feedback could achieve additional performance advantages without any extra measurements. The optimal multi-UAV deployment strategy is then proposed, by which the potential of the UAP system could be fully characterized. Last but not least, we verify our solutions via numerical simulations and practical experiments, which provide meaningful insights and performance evaluations to the system design and implementations.

High accuracy and seamless position information formulates the basis of many modern wireless applications, such as the Internet of Things (IoT) and intelligent transportation systems (ITSs). In this article, aiming at the ground user equipment (UE) those in the “blind spots,” where only limited navigation signals are provided, the temporary aerial-aided “anchors” such as the unmanned aerial vehicles (UAVs) are introduced as alternating solutions. We first give the general fundamental limits of the three-dimensional space–air–ground-integrated localization networks (SAGILNs) using both time and angle measurements. Unlike most existing investigations, we treat aerial nodes as “agents” whose positions are not known beforehand. We then try to formulate an efficient scheduling strategy, where proper networkbehaviors, including the resource optimization and UAV deployment, are provided. We find that the proposed scheduling problems could be formulated as standard semidefinite programming (SDP) problems and solved by off-the-shelf solvers. Numerical results are provided to validate our analysis. The proposed methods and analyses provide meaningful insights for performance benchmarks for the implementation of SAGILN.

In typical autonomous driving, a lane-level (submeter) accuracy and ubiquitous coverage is required. Since the signals from the widely adopted Global Navigation Satellite Systems (GNSS) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required. In this paper, we propose an unmanned aerial vehicle (UAV) aided vehicle positioning framework, combined with the general non-parametric belief propagation (NBP) method, to improve the positioning accuracy of vehicles at blind spots. Aiming at the data association issue during the UAV detection, a cluster-based two-step joint probabilistic data association (JPDA) method is adopted. Furthermore, we find that in the multi-UAV scenarios, proper messages feedback of vehicles can effectively improve the data association, then further enhance the positioning accuracy. Numerical results are provided, to validate our analysis, and show significant performance advantages.

Seamless high precision positioning formulates the basis for intelligent driving. Due to the frequent occurrence of blind spots for vehicles in urban areas, we try to introduce the unmanned aerial vehicle for ground vehicle localization assistance in this paper. We first formulate the general scheduling framework of the UAV-aided vehicle localization system. Since the proper deployment of UAVs is of great importance to the localization performance. We then give a resource allocation based UAV deployment method. High accuracy iterative algorithms are provided to solve the essential non-convex problem. Simulation results show the convergence of the proposed method in all investigated cases. Furthermore, obvious performance advantages can be achieved.