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This paper examines the Galileo Open Service Navigation Message Authentication (OSNMA) and, for the first time, discovers two critical vulnerabilities, namely artificially-manipulated time synchronization (ATS) and interruptible message authentication (IMA). ATS allows attackers falsify a receiver's signals and/or local reference time (LRT) while still fulfilling the time synchronization (TS) requirement. IMA allows temporary interruption of the navigation data authentication process due to the reception of a broken message (probably caused by spoofing attacks) and restores the authentication later. By exploiting the ATS vulnerability, we propose a TS-comply replay (TSR) attack with two variants (real-time and non-real-time), where attackers replay signals to a victim receiver while strictly complying with the TS rule. We further propose a TS-comply forgery (TSF) attack, where attackers first use a previously-disclosed key to forge a message based on the OSNMA protocol, then tamper with the vitcim receiver's LRT correspondingly to comply with the TS rule and finally transmit the forged message to the receiver. Finally, we propose a concatenating replay (CR) attack based on the IMA vulnerability, where attackers concatenate replayed signals to the victim receiver's signals in a way that still enables correct verification of the navigation data in the replayed signals. To validate the effectiveness of the proposed attacks, we conduct real-world experiments with a commercial Galileo receiver manufactured by Septentrio, two software-defined radio (SDR) devices, open-source Galileo-SDR-SIM and OSNMAlib software. The results showed that all the attacks can successfully pass the OSNMA scheme and the TSF attack can spoof receivers to arbitrary locations., Comment: 16 pages, 20 figures

Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements.

Structural health monitoring (SHM) is essential for the early detection of infrastructure defects, such as cracks in concrete bridge pier. but often faces challenges in efficiency and accuracy in complex environments. Although the Segment Anything Model (SAM) achieves excellent segmentation performance, its computational demands limit its suitability for real-time applications on edge devices. To address these challenges, this paper proposes Crack-EdgeSAM, a self-prompting crack segmentation system that integrates YOLOv8 for generating prompt boxes and a fine-tuned EdgeSAM model for crack segmentation. To ensure computational efficiency, the method employs ConvLoRA, a Parameter-Efficient Fine-Tuning (PEFT) technique, along with DiceFocalLoss to fine-tune the EdgeSAM model. Our experimental results on public datasets and the climbing robot automatic inspections demonstrate that the system achieves high segmentation accuracy and significantly enhanced inference speed compared to the most recent methods. Notably, the system processes 1024 x 1024 pixels images at 46 FPS on our PC and 8 FPS on Jetson Orin Nano.

Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images/videos. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face depth map, which has shown to be effective in other areas such as face recognition or face detection, is unfortunately paid little attention to in literature for face manipulation detection. In this paper, we explore the possibility of incorporating the face depth map as auxiliary information for robust face manipulation detection. To this end, we first propose a Face Depth Map Transformer (FDMT) to estimate the face depth map patch by patch from an RGB face image, which is able to capture the local depth anomaly created due to manipulation. The estimated face depth map is then considered as auxiliary information to be integrated with the backbone features using a Multi-head Depth Attention (MDA) mechanism that is newly designed. We also propose an RGB-Depth Inconsistency Attention (RDIA) module to effectively capture the inter-frame inconsistency for multi-frame input. Various experiments demonstrate the advantage of our proposed method for face manipulation detection., Comment: 12 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:2212.14230

This paper delves into the time-efficient covert multicast in a wireless communication system facilitated by Unmanned Aerial Vehicle (UAV), in which the UAV aims to disseminate a common covert information to multiple ground users (GUs) while suffering from the risk of detection by a ground warden (Willie). We propose one hop (OH) and two hop (TH) transmission schemes, first develop a theoretical framework for performance modeling of both the detection error probability at Willie and the transmission time at UAV. The optimization problems subject to the covertness constraint for the two transmission schemes are then formulated to gain insights into the system settings of the UAV's prior transmit probability, transmit power and horizontal location that affect the minimum transmission time. The optimization problems are non-convex and challenging to give numerical results. We thus explore the optimal setting of the transmit power and the prior transmit probability for the UAV separately under specific parameters with two schemes. We further propose a particle swarm optimization (PSO) based algorithm and an exhaustive algorithm to provide the joint solutions for the optimization problem with the OH transmission scheme and TH scheme, respectively. Finally, the efficiency of the proposed PSO-based algorithm is substantiated through extensive numerical results.

The spin-$3/2$ chain is a versatile prototypical platform for the study of competition between different kinds of magnetic orders, with the objective of obtaining a deeper understanding of the corresponding quantum phase transitions. In this work, we investigate the spin-$3/2$ chain with nearest-neighbor $J_1$, next-nearest-neighbor $J_2$, and uniaxial single-ion anisotropy $D$ terms in the absence of a magnetic field. For positive values of $J_2/J_1$ and $D/J_1$, we find seven different phases in a rich phase diagram. Without frustration $J_2=0$, a gapless Luttinger liquid phase remains stable for all $D>0$. As $J_2$ increases, we observe three phases with distinct dimerized valence bond orders, which show an intricate competition with vector chiral order and incommensurate correlations. For large $J_2$, regions of phase coexistence between the dimerized and vector chiral orders emerge. We present large-scale numerical data for the determination of transition lines, order parameters, and the nature of the phase transitions., Comment: 8 pages, 10 figures

Covert communication's effectiveness critically depends on precise channel state information (CSI). This paper investigates the impact of imperfect CSI on achievable covert communication performance in a two-hop relay system. Firstly, we introduce a two-hop covert transmission scheme utilizing channel inversion power control (CIPC) to manage opportunistic interference, eliminating the receiver's self-interference. Given that CSI estimation error (CEE) and feedback delay (FD) are the two primary factors leading to imperfect CSI, we construct a comprehensive theoretical model to accurately characterize their effects on CSI quality. With the aid of this model, we then derive closed-form solutions for detection error probability (DEP) and covert rate (CR), establishing an analytical framework to delineate the inherent relationship between CEE, FD, and covert performance. Furthermore, to mitigate the adverse effects of imperfect CSI on achievable covert performance, we investigate the joint optimization of channel inversion power and data symbol length to maximize CR under DEP constraints and propose an iterative alternating algorithm to solve the bi-dimensional non-convex optimization problem. Finally, extensive experimental results validate our theoretical framework and illustrate the impact of imperfect CSI on achievable covert performance.

This paper investigates the covert communication in an air-to-ground (A2G) system, where a UAV (Alice) can adopt the omnidirectional microwave (OM) or directional mmWave (DM) transmission mode to transmit covert data to a ground user (Bob) while suffering from the detection of an adversary (Willie). For both the OM and DM modes, we first conduct theoretical analysis to reveal the inherent relationship between the transmit rate/transmit power and basic covert performance metrics in terms of detection error probability (DEP), effective covert rate (ECR), and covert Shannon capacity (CSC). To facilitate the transmission mode selection at Alice, we then explore the optimization of transmit rate and transmit power for ECR/CSC maximization under the OM and DM modes, and further propose a hybrid OM/DM transmission mode which allows the UAV to adaptively select between the OM and DM modes to achieve the maximum ECR and CSC at a given location of UAV. Finally, extensive numerical results are provided to illustrate the covert performances of the concerned A2G system under different transmission modes, and demonstrate that the hybrid OM/DM transmission mode outperforms the pure OM or DM mode in terms of covert performance.

While the millimeter-wave (mmWave) communication is robust against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an eavesdropper can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

We introduce the notion of the twisted Segre product $A\circ_\psi B$ of $\mathbb Z$-graded algebras $A$ and $B$ with respect to a twisting map $\psi$. It is proved that if $A$ and $B$ are noetherian Koszul Artin-Schelter regular algebras and $\psi$ is a twisting map such that the twisted Segre product $A\circ_\psi B$ is noetherian, then $A\circ_\psi B$ is a noncommutative graded isolated singularity. To prove this result, the notion of densely (bi-)graded algebras is introduced. Moreover, we show that the twisted Segre product $A\circ_\psi B$ of $A=k[u,v]$ and $B=k[x,y]$ with respect to a diagonal twisting map $\psi$ is a noncommutative quadric surface (so in particular it is noetherian), and we compute the stable category of graded maximal Cohen-Macaulay modules over it., Comment: 23 pages, v2: a few typos were fixed, v3: minor updates

Let $A$ be a noetherian Koszul Artin-Schelter regular algebra, and let $f\in A_2$ be a central regular element of $A$. The quotient algebra $A/(f)$ is usually called a (noncommutative) quadric hypersurface. In this paper, we use the Clifford deformation to study the quadric hypersurfaces obtained from the tensor products. We introduce a notion of simple graded isolated singularity and proved that, if $B/(g)$ is a simple graded isolated singularity of 0-type, then there is an equivalence of triangulated categories $\underline{\text{mcm}}\,A/(f)\cong\underline{\text{mcm}}\,(A\otimes B)/(f+g)$ of the stable categories of maximal Cohen-Macaulay modules. This result may be viewed as a generalization of Kn\"{o}rrer's periodicity theorem. As an application, we study the double branch cover $(A/(f))^\#=A[x]/(f+x^2)$ of a noncommutative conic $A/(f)$.