In the evolving landscape of next-generation wireless networks, ensuring secure communications in covert military operations is paramount. This paper proposes an advanced localization-based security system utilizing passive receivers and Time Difference of Arrival (TDOA) techniques to continuously authenticate the signals of a commander in dynamic operational scenarios. Our system effectively counters physical layer spoofing attacks by distinguishing between the precise locations of a legitimate entity and potential adversaries. To that end, we derive the positioning error bound (PEB) specific to TDOA systems, emphasizing the critical impact of receiver arrangement on localization accuracy. Furthermore, we introduce a novel application of deep Q-learning for the NP-hard problem of optimal placement of receivers, addressing the challenge of spatial geometry, which significantly influences localization accuracy. Through extensive testing, we demonstrate that our proposed approach notably outperforms traditional placement methods in mitigating geometry-induced errors and enhancing overall localization precision. Ultimately, this facilitates realizing and maintaining a secure zone where users can authenticate each other through localization.

The use of transformers for vision tasks has challenged the traditional dominant role of convolutional neural networks (CNN) in computer vision (CV). For image classification tasks, Vision Transformer (ViT) effectively establishes spatial relationships between patches within images, directing attention to important areas for accurate predictions. However, similar to CNNs, ViTs are vulnerable to adversarial attacks, which mislead the image classifier into making incorrect decisions on images with carefully designed perturbations. Moreover, adversarial patch attacks, which introduce arbitrary perturbations within a small area (usually less than 3% of pixels), pose a more serious threat to ViTs. Even worse, traditional detection methods, originally designed for CNN models, are impractical or suffer significant performance degradation when applied to ViTs, and they generally overlook patch attacks.In this paper, we propose ViTGuard as a general detection method for defending ViT models against adversarial attacks, including typical attacks where perturbations spread over the entire input (Lpnorm attacks) and patch attacks. ViTGuard uses a Masked Autoencoder (MAE) model to recover randomly masked patches from the unmasked regions, providing a flexible image reconstruction strategy. Then, threshold-based detectors leverage distinctive ViT features, including attention maps and classification (CLS) token representations, to distinguish between normal and adversarial samples. The MAE model does not involve any adversarial samples during training, ensuring the effectiveness of our detectors against unseen attacks. ViTGuard is compared with seven existing detection methods under nine attacks across three datasets with different sizes. The evaluation results show the superiority of ViTGuard over existing detectors. Finally, considering the potential detection evasion, we further demonstrate ViTGuard’s robustness against adaptive attacks for evasion.

Geofencing technologies have emerged as crucial tools in establishing virtual boundaries within both physical and digital spaces, providing a secure method to manage and supervise specified zones. They are now recognized as vital instruments for delineating and managing boundaries in a range of applications, from ensuring aviation safety in drone operations to regulating access in mixed reality environments such as the metaverse. Successful geofencing depends significantly on accurate tracking, which is essential for preserving the integrity and effectiveness of these systems. Utilizing the benefits of 5G technology, such as its broad bandwidth and widespread availability, offers a promising approach to improve geofencing performance. In this paper, we present DEFENCE: Deep Reinforcement Learning for Geofencing Enhancement, an innovative method for precise geofencing that utilizes "5G Points" within indoor 5G small cell networks, optimally placed using a deep Q-learning framework. Through the computation of the Cramér-Rao Lower Bound (CRLB), we evaluate tracking errors arising from spatial configurations and ranging inaccuracies. Our proposed deep Q-learning model tackles the NP-hard challenge of identifying the optimal placement of 5G Points to reduce errors caused by spatial geometry. We implemented an extensive testing campaign to assess the efficacy of DEFENCE. Our findings reveal that this strategic deployment enhances tracking accuracy by a factor of 100 over conventional placement methods. This breakthrough considerably bolsters geofencing systems, enhancing their defense against potential threats such as unauthorized drone incursions and security breaches within metaverse environments.

In this paper, an innovative 3D visualization tool is proposed to facilitate the quickly browsing and understanding of the video sequence for users. Taking advantage of the major windows, our tool presents the multiscale key frames and the video content clearly and effectively. Namely, KF View provides a wonderful navigation of the video key frames with different levels of details. Frame View presents an interesting view of the whole video content. SIM View allows an expressive exploration of the similarities between key frames and also between key frames and the video frames. Importantly, together with many convenient and attractive interactions, this tool is quite efficient to help users grasp the video information soundly.

Geosat Follow On (GFO) radar altimeter data are processed with previous altimeter datasets to measure long-term elevation change of higher elevation portions of the southern Greenland ice sheet over time periods from 1978-1988, 1985-2002, and 1978-2002. Average elevation change results indicate approximately zero overall elevation change for all time periods. The results also indicate that upper-elevation thinning in southeast Greenland has been widespread and has existed for several decades.