Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.

Visible spectrum images capture limited information from just three discrete bands, often resulting in suboptimal performance in underwater depth estimation (UDE) due to significant information loss from water absorption. In contrast, HSIs, which include hundreds of continuous bands, provide abundant spectral information that offers greater resilience against the adverse effects of water absorption. In this paper, we conduct a comprehensive study to investigate how spectral information can enhance remote sensing UDE through two key aspects: the benchmark dataset and the general framework. For the benchmark dataset, we construct a real-world hyperspectral UDE (HUDE) dataset ATR-HUDE, comprising approximately 500 synchronized hyperspectral and LiDAR data pairs collected from diverse coastal scenes and flight altitudes. Regarding the general framework, we integrate recent advances in state space models and physical imaging models to design a novel HUDE framework named HUDEMamba that estimates underwater depth using both model-driven and data-driven approaches. Experimental results on the constructed benchmark dataset validate the potential of HUDE and the effectiveness of HUDEMamba.

Cross-Modality Re-Identification (VT-ReID) aims to achieve around-the-clock target matching, benefiting from the strengths of both RGB and infrared (IR) modalities. However, the field is hindered by limited datasets, particularly for vehicle VT-ReID, and by challenges such as modality bias training (MBT), stemming from biased pre-training on ImageNet. To tackle the above issues, this paper introduces an dataset benchmark, named UCM-VeID V2, for vehicle VT-ReID, and proposes a new self-supervised pre-training method, Cross-Modality Patch-Mixed Self-Supervised Learning (PMSL). UCM-VeID V2 dataset features a significant increase in data volume, along with enhancements in multiple aspects. PMSL addresses MBT by learning modality-invariant features through Patch-Mixed Image Reconstruction (PMIR) and Modality Discrimination Adversarial Learning (MDAL), and enhances discriminability with Modality-Augmented Contrasting Cluster (MACC). Comprehensive experiments are carried out to validate the proposed method.

Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep learning techniques and the availability of high-quality dataset. However, the existing dataset struggles to fully capture real-world complexity for limited imaging conditions. To this end, we introduce a high-diversity dataset ATR-UMOD covering varying scenarios, spanning altitudes from 80m to 300m, angles from 0° to 75°, and all-day, all-year time variations in rich weather and illumination conditions. Moreover, each RGB-IR image pair is annotated with 6 condition attributes, offering valuable high-level contextual information. To meet the challenge raised by such diverse conditions, we propose a novel prompt-guided condition-aware dynamic fusion (PCDF) to adaptively reassign multimodal contributions by leveraging annotated condition cues. By encoding imaging conditions as text prompts, PCDF effectively models the relationship between conditions and multimodal contributions through a task-specific soft-gating transformation. A prompt-guided condition-decoupling module further ensures the availability in practice without condition annotations. Experiments on ATR-UMOD dataset reveal the effectiveness of PCDF.

Blockchain enhances the robustness and user engagement of Federated Learning (FL) systems but fails to meet the throughput and real-time requirements for model transmission. While sharding architectures improve system throughput, the latency introduced by mainchain model transmission remains a performance bottleneck, compromising the QoS of FL systems. In this paper, we propose a Mainchain-Free Sharding architecture, MFSChain, featuring an adaptive sharding mechanism based on hierarchical clustering. This mechanism improves shard model performance by eliminating the need for mainchain aggregation (i.e., shard-level global models). We also introduce the Federated Learning State Tree (FLS-Tree) for client management and state migration without a mainchain, alongside a lightweight storage scheme, LiFLS-Tree. Through theoretical analysis and extensive simulations, we demonstrate that MFSChain outperforms traditional blockchain and sharding architectures. Specifically, MFSChain reduces client waiting time by 17% and 24%, increases average model accuracy by 2.5% to 10% compared to traditional global models, and boosts throughput by$464 \times$while reducing transaction processing latency by 99%.

Hyperspectral imaging (HSI) provides fine-grained spectral information essential for material identification and target detection, particularly in complex environments such as underwater scenarios. However, hyperspectral underwater target detection (HUTD) remains challenging due to severe spectral distortions and variability introduced by wavelength-dependent absorption and the dynamic nature of aquatic environments. Existing separation-based and characterization-based methods are often constrained by weak signal responses or a heavy reliance on accurate environmental parameter estimation, which is difficult to achieve in practice. To overcome these limitations, we propose PCL-HUTD, a novel physics-informed curriculum learning framework for robust underwater target characterization without requiring explicit environmental modeling. PCL-HUTD integrates a physics-guided target construction module with a hard-sample aware contrastive learning strategy, enhanced by unsupervised clustering and a perturbation-consistency based sample selection mechanism. Furthermore, a closed-loop curriculum learning paradigm is introduced to progressively refine target representations throughout training. Extensive experiments on three real-world HUTD datasets demonstrate that PCL-HUTD achieves state-of-the-art performance in both detection accuracy and robustness, particularly under challenging conditions with strong background interference. These results validate the effectiveness of our parameter-free, physics-informed approach for underwater hyperspectral target detection.

UAV-borne hyperspectral remote sensing has emerged as a promising technique for underwater target detection (UTD). Existing hyperspectral UTD (HUTD) methods in deep-sea environments typically rely on bathymetric models to characterize spectral attenuation caused by the water column, often coupled with restoration- or prediction-based pipelines for target detection. However, in nearshore environments, high turbidity and complex seabed topography introduce nonlinear and heterogeneous attenuation effects that cannot be accurately modeled by conventional bathymetric models. Moreover, the highly dynamic nature of nearshore waters induces significant spectral variability in target signatures, rendering restoration- and prediction-based pipelines ineffective due to their limited capacity to model such variability. To address these challenges, we propose the Hyperspectral Underwater Contrastive Learning Network (HUCLNet), a data-driven framework for HUTD that eliminates dependence on bathymetric models. Rather than modeling spectral attenuation, HUCLNet establishes a semantically meaningful latent space with enhanced target-background separability through a hybrid-level contrastive learning framework. A reliability-guided clustering strategy is introduced to refine the contrastive learning inputs and improve representation robustness. Furthermore, a self-paced learning mechanism flexibly integrates the clustering and contrastive modules to stabilize training and accelerate convergence. Extensive experiments demonstrate that HUCLNet outperforms state-of-the-art methods across various evaluation metrics.

Deep learning (DL) methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this article under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning (MSAL) module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network (GCN) with learnable graph structure to establish global pixel-wise combinations. In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect. Finally, to improve the defense transferability and address the problem of limited labeled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner. Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.

Rowhammer has drawn much attention from both academia and industry in the past years as rowhammer exploitation poses severe consequences to system security. Since the first comprehensive study of rowhammer in 2014, a number of rowhammer attacks have been demonstrated against dynamic random access memory (DRAM)-based commodity systems to break software confidentiality, integrity and availability. Accordingly, numerous software defenses have been proposed to mitigate rowhammer attacks on commodity systems of either legacy (e.g., DDR3) or recent DRAM (e.g., DDR4). Besides, multiple hardware defenses (e.g., Target Row Refresh) from the industry have been deployed into recent DRAM to eliminate rowhammer, which we categorize as production defenses.

Visible-infrared (RGB-IR) image fusion has shown great potentials in object detection based on unmanned aerial ve-hicles (UAVs). However, the weakly misalignment problem between multimodal image pairs limits its performance in object detection. Most existing methods often ignore the modality gap and emphasize a strict alignment, resulting in an upper bound of alignment quality and an increase of implementation costs. To address these challenges, we propose a novel method named Offset-guided Adaptive Feature Alignment (OAFA), which could adaptively adjust the relative positions between multimodal features. Considering the impact of modality gap on the cross-modality spa-tial matching, a Cross-modality Spatial Offset Modeling (CSOM) module is designed to establish a common sub-space to estimate the precise feature-level offsets. Then, an Offset-guided Deformable Alignment and Fusion (ODAF) module is utilized to implicitly capture optimal fusion po-sitions for detection task rather than conducting a strict alignment. Comprehensive experiments demonstrate that our method not only achieves state-of-the-art performance in the UAVs-based object detection task but also shows strong robustness to the weakly misalignment problem.

Based on the rapid development of edge computing and resource-constrained characteristics, new requirements for edge information system security evaluation are proposed. Oriented to the resource-constrained scenarios of edge information systems and the distribution characteristics of raw data for security evaluation, the adaptability of existing models is improved. The improved Principal Component Analysis (PCA-S method) based on Spearman’s Coefficient is proposed for index screening. In order to further improve the screening effect and reduce the resource consumption of the security evaluation model, the PCA-S method and the Distinction Degree Screening method are combined by taking the intersection, and the PCA-SDC combination screening method is proposed. Through the PCA-SDC index combination screening, the Coefficient of Variation weighting, and the Fuzzy Comprehensive Evaluation, the PSDC-CVF edge information system security evaluation model is finally formed. In order to assess the effect of index screening and security evaluation, three indicators, namely, the improved Average Quantity of Information Change Degree, the Average Information Contribution Change Degree, and the Fuzzy Evaluation Deviation Degree, are proposed. Through experiments, the improved PCA-S method and the combination screening PCA-SDC method are sequentially proved to be well adapted and effective in the index screening process of edge information system security evaluation. It is also verified that the PSDC-CVF model reduces the resource consumption compared with the traditional model and better balances the model energy consumption and performance.

Convolutional neural networks (CNNs) have shown their potential ability to extract discriminative features for hyperspectral image classification. However, traditional deep learning methods using CNNs tend to overlook the influence of complex environmental factors. These factors contribute to an increase in intraclass variance and a decrease in interclass variance, making it considerably more challenging to extract meaningful features. To overcome this problem, this work develops a novel deep intrinsic decomposition with adversarial learning, namely AdverDecom, for hyperspectral image classification to mitigate the negative impact of environmental factors on classification performance. First, we develop a generative network for hyperspectral images (HyperNet) to extract the environment-related features and category-related features from the image. Then, a discriminative network is constructed to distinguish different environmental categories. Finally, an environment-category joint learning loss is developed for adversarial learning to make the deep model learn discriminative features. Experiments are conducted over four commonly used real-world datasets and the comparison results show the superiority of the proposed method. The implementation of the proposed method could be accessed athttps://github.com/shendu-sw/Adversarial_Learning_Intrinsic_Decompositionfor the sake of reproducibility.

Hyperspectral underwater target detection (HUTD) is a promising and challenging task in remote sensing image processing. Existing methods face significant challenges when adapting to nearshore environments, where cluttered backgrounds hinder the extraction of target signatures and exacerbate signal distortion. Hyperspectral unmixing (HU) demonstrates potential effectiveness for nearshore underwater target detection (UTD) by simultaneously extracting water background endmembers and separating target signals. To this end, this article investigates a novel nonlinear unmixing network for hyperspectral UTD, denoted as nonlinear unmixing network for hyperspectral-UTD (NUN-UTD), in which a well-designed autoencoder-based unmixing network is used to obtain the abundance map as the detection result. To address the weak underwater target signals, a target prior spectral preservation scheme is employed to guide the unmixing network in learning the accurate target abundance. Besides, to address the complexity of the nearshore environment, a pseudomixed data classification constraint is incorporated into the objective function to enhance the discriminative capability between the background and the target. Moreover, we adopt an additive postnonlinear model in the decoder to deal with the interactions between underwater spectra to account for the nonlinear effects between spectra of underwater substances. To validate the effectiveness of the proposed method, we constructed a hyperspectral dataset for nearshore UTD. Extensive experiments conducted on three real-world datasets and one simulated dataset demonstrate that our method achieves outstanding performance in HUTD.

Vision transformers (ViTs) have achieved great success in unmanned aerial vehicle (UAV) target detection tasks. However, little attention has been paid to the adversarial attack against ViT-based detectors, and the generated adversarial examples cannot take physical realizability and attack transferability into account at the same time. To overcome the limitation, we focus on transferable attacks toward ViT-based detectors in optical UAV-based remote sensing images and generate adversarial examples in the physical world. Concretely, we design unique perturbation patches deployed within and beyond the target object rather than requiring the patches to be aligned with image tokens. To narrow the gap between limited digital samples and complex physical scenarios, we conduct data augmentation on training images at global and local levels. In addition, we propose a novel transferable attack method named Jacobian matrix regularization (JMR), which consists of feature variance regularization (FVR) and attention weight regularization (AWR). Specifically, FVR calculates feature variances of different channels within specific layers and then sets the features as zeros for channels with top variances. AWR is achieved by masking the largest self-attention weights. We conduct extensive transferable experiments with typical detectors in both digital and physical UAV-based remote sensing scenarios. The results indicate that our method could achieve competitive transferability compared with state-of-the-art methods.

Owing to the capacity of performing full-time target searches, cross-modality vehicle re-identification based on unmanned aerial vehicles (UAV) is gaining more attention in both video surveillance and public security. However, this promising and innovative research has not been studied sufficiently due to the issue of data inadequacy. Meanwhile, the cross-modality discrepancy and orientation discrepancy challenges further aggravate the difficulty of this task. To this end, we pioneer a cross-modality vehicle Re-ID benchmark named UAV Cross-Modality Vehicle Re-ID (UCM-VeID), containing 753 identities with16015RGB and13913infrared images. Moreover, to meet cross-modality discrepancy and orientation discrepancy challenges, we present a hybrid weights decoupling network (HWDNet) to learn the shared discriminative orientation-invariant features. For the first challenge, we proposed a hybrid weights siamese network with a well-designed weight restrainer and its corresponding objective function to learn both modality-specific and modality shared information. In terms of the second challenge, three effective decoupling structures with two pretext tasks are investigated to flexibly conduct orientation-invariant feature separation task. Comprehensive experiments are carried out to validate the effectiveness of the proposed method.

Abstract Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise generation. This would make these deep models unable to generate discriminative features and provide impressive classification performance. To leverage such intrinsic information, this work develops a novel deep learning framework with the noise inclined module and denoise framework for hyperspectral image classification. First, the spectral signature of hyperspectral image is modeled with the physical noise model to describe the high intra‐class variance of each class and great overlapping between different classes in the image. Then, a noise inclined module is developed to capture the physical noise within each object and a denoise framework is then followed to remove such noise from the object. Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image. Experiments are conducted over two commonly used real‐world datasets and the experimental results show the effectiveness of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu‐sw/noise‐physical‐framework .

Compared with existing vehicle reidentification (VeID) tasks conducted with datasets collected by fixed surveillance cameras, VeID for an unmanned aerial vehicle (UAV) is still under-explored and could be more challenging. Vehicles with the same color and type show extremely similar appearances from the UAV’s perspective so that mining fine-grained characteristics becomes necessary. Recent works tend to extract distinguishing information by regional features and component features. The former requires input images to be aligned and the latter entails detailed annotations, both of which are difficult to meet in UAV application. To extract efficient fine-grained features and avoid tedious annotating work, this letter develops an unsupervised self-aligned network consisting of three branches. The network introduced a self-alignment module to convert the input images with variable orientations to a uniform orientation, which is implemented under the constraint of a triple loss function designed with spatial features. On this basis, spatial features, obtained by vertical and horizontal segmentation methods, and global features are integrated to improve the representation ability in embedded space. Extensive experiments are conducted on UAV-VeID dataset, and our method achieves the best performance compared with recent reidentification (ReID) works.

The research on attack transferability is of great importance as it can guide how to conduct an adversarial attack without knowing any information about target models. However, it remains challenging for adversarial examples to maintain a good attack transferability performance, especially for the black-box attack implemented in the physical world. To enhance black-box transferability of physical attacks on object detectors, we present a novel adversarial learning method to produce adversarial patches by redistributing separable attention maps. Concretely, we first develop smoothed multilayer attention maps by introducing serial composite transformations, which could suppress model-specific noise on the one hand, and cover objects to be concealed at various resolutions on the other hand. Besides, our method resorts to a scalable mask to separate object attention from the background and adjust their distribution with a novel loss function. Extensive experiments show that our approach outperforms state-of-the-art methods in both the digital space and the physical world. Our code is available at https://github.com/zhangyu13a/transPhyAtt .

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Vehicle re-identification (VeID) has attracted a growing research interest in recent years, and excellent performance has been shown with fixed traffic cameras. However, vehicle ReID in aerial images taken by unmanned aerial vehicles (UAVs), possessing both variable locations and special viewpoints, is still under-explored. Recent works tend to extract meaningful local features by careful annotation, which are effective but time-consuming. In order to extract discriminative features and avoid tedious annotating work, this letter develops an attention mask (AM)-based network with simple color annotation for object enhancement and background reduction. The network makes full use of deep features obtained by a pretrained color classification network and then utilizes principal component analysis (PCA) as a mapping function to achieve AMs without partial annotation. Besides, we introduce weighted triplet loss (WTL) function to deal with the problem of great similarity between classes caused by overlook views of UAVs. The loss function concentrates more on negative pairs to facilitate the identification ability of network. Rich experiments are conducted on both UAV dataset and surveillance dataset, and our method achieves competitive performance compared with recent ReID works.

In this paper, we proposed a novel underwater target detection method based on hyperspectral unmixing methodology which composes of three different modules. The first module employed a classical anomaly detector to pick out the target-water mixed pixels from background, which can remove the influence of background pixels at the same time. Then, a bathymetric model-based autoencoder is designed in the second module to unmix target-water mixed pixels for attaining underwater target spectra without any prior information about water environment. Finally, we desgin a multi-criterion spectrum distance metric for target spectrum recognition and get the final detection result. Experimental results on simulated data sets demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art underwater target detection methods.