Search Results (11)

The palm-vein recognition system has garnered attention as a biometric technology due to its resilience to external environmental factors, protection of personal privacy, and low risk of external exposure. However, with recent advancements in deep learning-based generative models for image synthesis, the quality and sophistication of fake images have improved, leading to an increased security threat from counterfeit images. In particular, palm-vein images acquired through near-infrared illumination exhibit low resolution and blurred characteristics, making it even more challenging to detect fake images. Furthermore, spoof detection specifically targeting palm-vein images has not been studied in detail. To address these challenges, this study proposes the Fourier-gated feature-fusion network (FGFNet) as a novel spoof detector for palm-vein recognition systems. The proposed network integrates masked fast Fourier transform, a map-based gated feature fusion block, and a fast Fourier convolution (FFC) attention block with global contrastive loss to effectively detect distortion patterns caused by generative models. These components enable the efficient extraction of critical information required to determine the authenticity of palm-vein images. In addition, fractal dimension estimation (FDE) was employed for two purposes in this study. In the spoof attack procedure, FDE was used to evaluate how closely the generated fake images approximate the structural complexity of real palm-vein images, confirming that the generative model produced highly realistic spoof samples. In the spoof detection procedure, the FDE results further demonstrated that the proposed FGFNet effectively distinguishes between real and fake images, validating its capability to capture subtle structural differences induced by generative manipulation. To evaluate the spoof detection performance of FGFNet, experiments were conducted using real palm-vein images from two publicly available palm-vein datasets—VERA Spoofing PalmVein (VERA dataset) and PLUSVein-contactless (PLUS dataset)—as well as fake palm-vein images generated based on these datasets using a cycle-consistent generative adversarial network. The results showed that, based on the average classification error rate, FGFNet achieved 0.3% and 0.3% on the VERA and PLUS datasets, respectively, demonstrating superior performance compared to existing state-of-the-art spoof detection methods. Full article

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article

Global Navigation Satellite System (GNSS) spoofing induces the target receiver to obtain the wrong positioning and timing results, which is very harmful. It is necessary to develop high-precision GNSS spoofing detection and associated direction-finding methods. In order to achieve sensitive and high-precision direction-finding for GNSS spoofing, it is necessary to realize the spoofing signal detection in the capture phase. This paper first proposes a method of GNSS spoofing detection, based on machine learning, that extracts features in the capture phase, which realizes various types of spoofing detection such as matching power, carrier phase alignment, and frequency locking. Notably, existing spoofing-direction-finding methods are mainly based on dedicated antenna arrays, which incur high costs and are not conducive to large-scale deployments. The basis of the spoofing detection proposed by this paper consists of a differential phase-center correction method, which is proposed in the context of an off-the-shelf anti-jamming array antenna, which effectively reduces the impact of the phase-center jitter introduced by the mutual coupling between antenna arrays on the direction-finding. The publicly accessible Texas Spoofing Test Battery (TEXBAT) dataset and actual measured data are both used for test verification. The results demonstrate that the proposed spoofing detection method can achieve success rates of over 97% on the TEXBAT dataset and more than 96% on the measured dataset, and the accuracy of the proposed direction-finding method can reach 1°, which can realize the effective detection and direction-finding of GNSS spoofing. Full article

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently. Full article

Metamaterial analogues of electromagnetically induced transparency (EIT) enable a unique avenue to endow a coupled resonator system with quantum interference behavior, exhibiting remarkable properties in slow-wave and highly sensitive sensing. In particular, tunable and ultracompact-chip-integrated EIT-like effects reveal fantastic application prospects in plasmonic circuits and networks. Here, we demonstrate an electrically tuned on-chip EIT analogue by means of dynamic EIT modules side-coupled to ultrathin corrugated metallic strips supporting spoof surface plasmon polaritons (SSPPs). By embedding PIN diodes into the subradiant mode, on-to-off control of the destructive coupling between the radiative and subradiant modes results in dynamic chip-scale EIT-like behavior under the change of the bias voltage, allowing for an electrically tunable group delay of the surface waves. The physical mechanism of the active modulation is elucidated with the coupled mode theory. In addition, the cascaded capacity performed by installing multiple EIT modules with an interval of equivalent wavelength are also characterized on a planar plasmonic waveguide. The proposed system will pave a versatile route toward dynamic control in chip-scale functional devices. Full article

As the popularity of Internet of Things (IoT) increases, there is a considerable demand for the improvement of physical security, owing to the increase in edge devices. However, fabrication and measurement techniques used by attackers are also improving continuously, and hence, it is becoming increasingly difficult to ensure the security of each device using conventional approaches. To counter variable attacks in this context, the concept of nano-photonic metrics has been proposed, which is based on a functional collaboration between existing physical security and near-field optical techniques. In this approach, the optical signals obtained from optical near-field interactions, which are induced between the target with nano-scale structures and the tip of the scanning probe as the reader, are defined as the unique features of each device to be authenticated. When attackers attempt spoofing, they must fabricate not only clones of original nano-scale structures but also the scanning probe; otherwise, they cannot impersonate regular users. Moreover, the estimation of the nano-scale structures of the target and the characteristics of the probe is typically a complex, inverse problem. Therefore, a novel authentication is expected to be performed. In this paper, we report the results of the quantitative evaluations of the performance from the viewpoint of physical security and the experimental verification of the practicality of the proposed approach. Full article

The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone. Automated driving and ADAS features rely on data transferred over the CAN network from multiple sensors mounted on the vehicle. Verifying the integrity of the sensor data is essential for the safety and security of occupants and the proper functionality of these applications. Though the CAN interface ensures reliable data transfer, it lacks basic security features, including message authentication, which makes it vulnerable to a wide array of attacks, including spoofing, replay, DoS, etc. Using traditional cryptography-based methods to verify the integrity of data transmitted over CAN interfaces is expected to increase the computational complexity, latency, and overall cost of the system. In this paper, we propose a light-weight alternative to verify the sensor data’s integrity for vehicle applications that use CAN networks for data transfers. To this end, a framework for 2-dimensional quantization index modulation (2D QIM)-based data hiding is proposed to achieve this goal. Using a typical radar sensor data transmission scenario in an autonomous vehicle application, we analyzed the performance of the proposed framework regarding detecting and localizing the sensor data tampering. The effects of embedding-induced distortion on the applications using the radar data were studied through a sensor fusion algorithm. It was observed that the proposed framework offers the much-needed data integrity verification without compromising on the quality of sensor fusion data and is implemented with low overall design complexity. This proposed framework can also be used on any physical network interface other than CAN, and it offers traceability to in-vehicle data beyond the scope of the in-vehicle applications. Full article

Recently, the vulnerability of automatic speaker recognition systems to spoofing attacks has received significant interest among researchers. A robust speaker recognition system demands not only high recognition accuracy but also robustness to spoofing attacks. Several spoofing and countermeasure challenges have been organized to draw attention to this problem among the speaker recognition communities. Low-level descriptors designed to detect artifacts in spoofed speech are found to be the most effective countermeasures against spoofing attacks. In this work, we used Fisher vector encoding of low-level descriptors extracted from speech signals. The idea behind Fisher vector encoding is to determine the amount of change induced by the descriptors of the signal on a background probability model which is typically a Gaussian mixture model. The Fisher vector encodes the amount of change of the model parameters to optimally fit the new- coming data. For performance evaluation of the proposed approach we carried out spoofing detection experiments on the 2015 edition of automatic speaker verification spoofing and countermeasure challenge (ASVspoof2015) and report results on the evaluation set. As baseline systems, we used the standard Gaussian mixture model and i-vector/PLDA paradigms. For a fair comparison, in all systems, Constant Q cepstral coefficient (CQCC) features were used as low-level descriptors. With the Fisher vector-based approach, we achieved an equal error rate (EER) of 0.1145% on the known attacks, 1.223% on the unknown attacks, and 0.668% on the average. Moreover, with a single decision threshold this approach yielded an EER of 1.05% on the evaluation set. Full article

In Global Navigation Satellite System (GNSS), a spoofing attack consists of forged signals which possibly cause the attacked receivers to deduce a false position, a false clock, or both. In contrast to simplistic spoofing, the induced spoofing captures the victim tracking loops by gradually adjusting it’s parameters, e.g., code phase and power. Then the victims smoothly deviates from the correct position or timing. Therefore, it is more difficult to detect the induced spoofing than the simplistic one. In this paper, by utilizing the dynamic nature of such gradual adjustment process, an induced spoofing detection method is proposed based on the S-curve-bias (SCB). Firstly, SCB in the inducing process is theoretically derived. Then, in order to detect the induced spoofing, a detection metric is defined. After that, a series of experiments using the Texas spoofing test battery (TEXBAT) are performed to demonstrate the effectiveness of the proposed algorithm. Full article

The loosely coupled integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have been widely used to improve the accuracy, robustness and continuity of navigation services. However, the integration systems possibly affected by spoofing attacks, since integration algorithms without spoofing detection would feed autonomous INSs with incorrect compensations from the spoofed GNSSs. This paper theoretically analyzes and tests the performances of GNSS/INS loosely coupled integration systems with the classical position fusion and position/velocity fusion under typical meaconing (MEAC) and lift-of-aligned (LOA) spoofing attacks. Results show that the compensations of Inertial Measurement Unit (IMU) errors significantly increase under spoofing attacks. The compensations refer to the physical features of IMUs and their unreasonable increments likely result from the spoofing-induced inconsistency of INS and GNSS measurements. Specially, under MEAC attacks, the IMU error compensations in both the position-fusion-based system and position/velocity-fusion-based system increase obviously. Under LOA attacks, the unreasonable compensation increments are found from the position/velocity-fusion-based integration system. Then a detection method based on IMU error compensations is tested and the results show that, for the position/velocity-fusion-based integration system, it can detect both MEAC and LOA attacks with high probability using the IMU error compensations. Full article

Nanophotonic technique has been attracting much attention in applications of nano-bio-chemical sensing and energy conversion of solar energy harvesting and enhanced energy transfer. One approach for nano-bio-chemical sensing is surface plasmon resonance (SPR) imaging, which can detect the material properties, such as density, ion concentration, temperature, and effective refractive index in high sensitivity, label-free, and real-time under ambient conditions. Recent study shows that SPR can successfully detect the concentration variation of nanofluids during evaporation-induced self-assembly process. Spoof surface plasmon resonance based on multilayer metallo-dielectric hyperbolic metamaterials demonstrate SPR dispersion control, which can be combined with SPR imaging, to characterize high refractive index materials because of its exotic optical properties. Furthermore, nano-biophotonics could enable innovative energy conversion such as the increase of absorption and emission efficiency and the perfect absorption. Localized SPR using metal nanoparticles show highly enhanced absorption in solar energy harvesting. Three-dimensional hyperbolic metamaterial cavity nanostructure shows enhanced spontaneous emission. Recently ultrathin film perfect absorber is demonstrated with the film thickness is as low as ~1/50th of the operating wavelength using epsilon-near-zero (ENZ) phenomena at the wavelength close to SPR. It is expected to provide a breakthrough in sensing and energy conversion applications using the exotic optical properties based on the nanophotonic technique. Full article