The Segment Anything Model (SAM) is a widely used vision foundation model with diverse applications, including image segmentation, detection, and tracking. Given SAM's wide applications, understanding its robustness against adversarial attacks is crucial for real-world deployment. However, research on SAM's robustness is still in its early stages. Existing attacks often overlook the role of prompts in evaluating SAM's robustness, and there has been insufficient exploration of defense methods to balance the robustness and accuracy. To address these gaps, this paper proposes an adversarial robustness framework designed to evaluate and enhance the robustness of SAM. Specifically, we introduce a cross-prompt attack method to enhance the attack transferability across different prompt types. Besides attacking, we propose a few-parameter adaptation strategy to defend SAM against various adversarial attacks. To balance robustness and accuracy, we use the singular value decomposition (SVD) to constrain the space of trainable parameters, where only singular values are adaptable. Experiments demonstrate that our cross-prompt attack method outperforms previous approaches in terms of attack success rate on both SAM and SAM 2. By adapting only 512 parameters, we achieve at least a 15% improvement in mean intersection over union (mIoU) against various adversarial attacks. Compared to previous defense methods, our approach enhances the robustness of SAM while maximally maintaining its original performance.

Due to the urgent need of the robustness of deep neural networks (DNN), numerous existing open-sourced tools or platforms are developed to evaluate the robustness of DNN models by ensembling the majority of adversarial attack or defense algorithms. Unfortunately, current platforms can neither optimize the DNN architectures nor the configuration of adversarial attacks to further enhance the model robustness or the performance of adversarial attacks. To alleviate these problems, in this paper, we propose a novel platform called auto-adversarial attack and defense ($A^{3}D$A3D), which can help search for robust neural network architectures and efficient adversarial attacks. $A^{3}D$A3D integrates multiple neural architecture search methods to find robust architectures under different robustness evaluation metrics. Besides, we provide multiple optimization algorithms to search for efficient adversarial attacks. In addition, we combine auto-adversarial attack and defense together to form a unified framework. Among auto adversarial defense, the searched efficient attack can be used as the new robustness evaluation to further enhance the robustness. In auto-adversarial attack, the searched robust architectures can be utilized as the threat model to help find stronger adversarial attacks. Experiments on CIFAR10, CIFAR100, and ImageNet datasets demonstrate the feasibility and effectiveness of the proposed platform.

Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, limiting their generalization in practice. In this paper, we propose a novel universal multi-view black-box attack against object detectors, which optimizes a universal adversarial UV texture constructed by multiple image stickers for a 3D object via the designed layout optimization algorithm. Specifically, we treat the placement of image stickers on the UV texture as a circle-based layout optimization problem, whose objective is to find the optimal circle layout filled with image stickers so that it can deceive the object detector under the multi-view scenario. To ensure reasonable placement of image stickers, two constraints are elaborately devised. To optimize the layout, we adopt the random search algorithm enhanced by the devised important-aware selection strategy to find the most appropriate image sticker for each circle from the image sticker pools. Extensive experiments conducted on four common object detectors suggested that the detection performance decreases by a large magnitude of 74.29% on average in multi-view scenarios. Additionally, a novel evaluation tool based on the photo-realistic simulator is designed to assess the texture-based attack fairly.

The Satellite-based Internet of Things (S-IoT) system offers a promising solution for accessing IoT devices in areas where terrestrial networks are inaccessible. These devices have significant roles in various scenarios, including aeronautic and maritime surveillance, data collection from sensors, and distress signals, from sinking ships. However, covering numerous IoT targets with a minimal number of satellites poses a challenge. This article proposes a solution known as the 5S payload designed for S-IoT. The 5S payload integrates five subsystems into a single unit, comprising the following components: 1) automatic identification system (AIS) for ship surveillance; 2) very high-frequency data exchange system (VDES) enabling two-way communication for ships; 3) automatic dependent surveillance-broadcast (ADS-B) system for aircraft surveillance; 4) data collection system (DCS) for user-defined sensors; and 5) emergency search and rescue (ESR) system. The design of the 5S payload adheres to the CubeSat standard and occupies a compact 1.5U size, enabling rapid manufacturing and deployment. The feasibility of the 5S payload was demonstrated on the TianTuo-5 satellite launched on 23 August 2020. Additionally, this article presents in-orbit experimental results spanning three years to further illustrate the effectiveness of the 5S payload.

The Shortest Distance Path (SDP) problem is a critical routing issue in communication networks, particularly in satellite networks. Typically, SDP is solved by graph-based iterative algorithms, while an explicit or analytic approach is challenging. However, considering the orbit dynamics and topology regularity, this paper proposes, for the first time, an explicit analytic phase-based algorithm STEPCLIMB to directly solve the SDP in low-Earth orbit (LEO) satellite networks. Based on the relationship between satellite phase and inter-satellite link distance, the SDP is modeled with the satellite phase, and SDP problem is converted into a total phase offset problem through theoretical derivations. Then STEPCLIMB is derived in two cases, respectively. Monte-Carlo simulations verify STEPCLIMB’s accuracy, which has zero error in the mono-valley case and has less than 0.1% error in the bi-valley case. The algorithm performs better in larger-scale constellations and can save over 99.4% computational cost compared to Dijkstra algorithm. Further, the SDP pattern and features in Starlink constellation are analyzed. The model proves that most inter-plane hops in the SDP occur successively, and the simulations further indicate that these hops prefer satellites in the higher latitude regions.

Deep neural networks (DNNs) are shown to be vulnerable to universal adversarial perturbations (UAP), a single quasi-imperceptible perturbation that deceives the DNNs on most input images. The current UAP methods can be divided into data-dependent and data-independent methods. The former exhibits weak transferability in black-box models due to overly relying on model-specific features. The latter shows inferior attack performance in white-box models as it fails to exploit the model's response information to benign images. To address the above issues, this paper proposes a novel universal adversarial attack to generate UAP with strong transferability by disrupting the model-agnostic features (e.g., edges or simple texture), which are invariant to the models. Specifically, we first devise an objective function to weaken the significant channel-wise features and strengthen the less significant channel-wise features, which are partitioned by the designed strategy. Furthermore, the proposed objective function eliminates the dependency on labeled samples, allowing us to utilize out-of-distribution (OOD) data to train UAP. To enhance the attack performance with limited training samples, we exploit the average gradient of the mini-batch input to update the UAP iteratively, which encourages the UAP to capture the local information inside the mini-batch input. In addition, we introduce the momentum term to accumulate the gradient information at each iterative step for the purpose of perceiving the global information over the training set. Finally, extensive experimental results demonstrate that the proposed methods outperform the existing UAP approaches. Additionally, we exhaustively investigate the transferability of the UAP across models, datasets, and tasks.

Recent work has demonstrated that pretrained transformers are overconfident in text classification tasks, which can be calibrated by the famous post-hoc calibration method temperature scaling (TS). Character or word spelling mistakes are frequently encountered in real applications and greatly threaten transformer model safety. Research on calibration under noisy settings is rare, and we focus on this direction. Based on a toy experiment, we discover that TS performs poorly when the datasets are perturbed by slight noise, such as swapping the characters, which results in distribution shift. We further utilize two metrics, predictive uncertainty and maximum mean discrepancy (MMD), to measure the distribution shift between clean and noisy datasets, based on which we propose a simple yet effective transferable TS method for calibrating models dynamically. To evaluate the performance of the proposed methods under noisy settings, we construct a benchmark consisting of four noise types and five shift intensities based on the QNLI, AG-News, and Emotion tasks. Experimental results on the noisy benchmark show that (1) the metrics are effective in measuring distribution shift and (2) transferable TS can significantly decrease the expected calibration error (ECE) compared with the competitive baseline ensemble TS by approximately 46.09%.

Physical adversarial attacks against deep neural networks (DNNs) have recently gained increasing attention. The current mainstream physical attacks use printed adversarial patches or camouflage to alter the appearance of the target object. However, these approaches generate conspicuous adversarial patterns that show poor stealthiness. Another physical deployable attack is the optical attack, featuring stealthiness while exhibiting weakly in the daytime with sunlight. In this paper, we propose a novel Reflected Light Attack (RFLA), featuring effective and stealthy in both the digital and physical world, which is implemented by placing the color transparent plastic sheet and a paper cut of a specific shape in front of the mirror to create different colored geometries on the target object. To achieve these goals, we devise a general framework based on the circle to model the reflected light on the target object. Specifically, we optimize a circle (composed of a coordinate and radius) to carry various geometrical shapes determined by the optimized angle. The fill color of the geometry shape and its corresponding transparency are also optimized. We extensively evaluate the effectiveness of RFLA on different datasets and models. Experiment results suggest that the proposed method achieves over 99% success rate on different datasets and models in the digital world. Additionally, we verify the effectiveness of the proposed method in different physical environments by using sunlight or a flashlight.

Collective swarm behavior recognition is the state-of-the-art to identify and observe a moving self-organized swarm or an object. However, because of the complex and harsh environment, current swarm behavior recognition systems remain either need large numbers of training samples or lack reliable measurements enough. In this paper, we solve the two issues in different directions, with the novel Support Vector Machines to provide guaranteed classification under the limited sample and a new conformal prediction algorithm to enhance the accuracy of the prediction. We report two classification schemes, both the simple prediction corresponding to confidence and credibility measures, and provide set prediction with any significance levels. We show why our approach performs better than other solutions through empirical studies with two benchmark datasets, both simulation and real-life swarm behavior datasets. To the best of our knowledge, we are the first to apply conformal prediction for the collective swarm behavior recognition purpose in general, and for the real-life swarm behavior in particular.

This paper introduces a martingales-protected paradigm for machine learning algorithms to solve swarm behavior classification problems. While regular machine learning algorithms, such as Neural Networks, Random Forests, and Decision Trees try to induce a general decision function for a learning task, the martingales-protected machine learning algorithms consider a set of fuzzy decision functions. The paper analyzes why the martingales-protected paradigm is well suited for most existing machine learning algorithms. These theoretical findings are supported by experiments on three swarm test collections. The experiments show substantial improvements over regular machine learning algorithms and present an empirical comparison of the new method and the classical learning approach for predictive modeling of real-life swarm behavior data. These comparisons suggest that the proposed martingale-protected method consistently performs better predictive performance than classical predictive modeling.

Full-state estimation with a limited number of sen-sors is a valuable and challenging task in monitoring and con-trolling complex physical systems. Supervised learning methods based on deep neural networks have shown excellent performance by learning the nonlinear mapping from sparse observations to global field. However, The neural network is a black box with weak explanation for physical processes, and the reconstruction performance is limited to the architecture and optimization of neural network. This paper aims to leverage the structure and laws inherent in data to reconstruct the global field by solving optimization problems instead of single network learning. We propose a unified global field reconstruction framework consisting of neural network prediction, proper orthogonal de-composition (POD), and linear optimization problem solving. The deep neural network is first trained to provide referenced global fields, which are combined with exact observations and the reference modes extracted by POD to establish a linear optimization problem. The objective of optimization problem is to superpose POD modes to satisfy the values of observations and referenced fields. The experiments conducted on fluid and thermal field reconstruction problems show that the proposed unified framework can significantly improve the reconstruction accuracy of neural networks and boost the performance of directly solving optimization problems without referenced fields.

Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle’s surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the nonplanar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.

Megaconstellation networks, represented by Starlink and OneWeb, have become a promising solution for the wide-area Internet of Things (IoT). IoT messages collected by the satellite can be routed to the ground gateway via multiple intersatellite link (ISL) relays and then access the ground network. Compared to traditional constellations, megaconstellations with massive satellites require more ISL relays that are greatly affected by the number and location of ground gateways. In this article, we focus on the ISL usage for connecting satellites and gateways and propose a novel method with low computation cost to evaluate the ISL usage metric. Then, we formulate a mixed-integer optimization model for the gateway site optimization (GSO) problem with minimizing the overall ISL usage, which is then simplified through model transformation. An IBD-PSO algorithm is proposed to solve the transformed GSO problem. Based on the Starlink constellation, simulation results have verified the proposed method by comparisons with previous studies. We further investigate how the gateway placement is affected by the gateway number and traffic demand pattern. The relations between gateway placement and ISL hop count of different satellites are also studied.

This article proposes a new network approach toward the implementation of Takagi–Sugeno (T–S) fuzzy models referred to as disjunctive fuzzy neural networks (DJFNNs). The proposed DJFNN involves a novel network architecture and a greedy learning algorithm. Being different from the existing grid-based and clustering-based network architectures, the proposed architecture adds an OR neural layer positioned between the fuzzification layer and the rule layer. In this way, the implied constraint between the number of rules and the number of fuzzy labels is excluded so that a curse of dimensionality can be overcome and more interpretable models are formed. Furthermore, inspired by the core algorithm for building a decision tree, a top–down, nonbacktracking, and greedy algorithm is proposed to learn the unknown parameters of the networks. The input space splits into smaller and smaller subspace along the predefined fuzzy grids in a supervised manner meanwhile the associated conditions of the T–S fuzzy model are identified. The greedy algorithm is applicable to high-dimensional problems since there is no exponential growth in time or space as the dimensionality increases. The new network architecture and greedy learning algorithm make the proposed DJFNN a regression model of high interpretability and good prediction capability, particularly suitable for solving the high-dimensional problems. The DJFNN was experimented with using a synthetic dataset and 28 real-world datasets and compared with classical and state-of-the-art methods through nonparametric statistical tests. The results confirmed the effectiveness of the DJFNN in terms of accuracy, interpretability, and computational cost.

The satellite layout optimization design (SLOD) problem is a kind of three-dimensional layout problems with complex performance constraints and known as a NP-hard problem. To solve SLOD problems efficiently and effectively, two types of hybrid optimization algorithm based on differential evolution (DE) are proposed in this paper. Concerning the design requirements of satellite attitude control subsystem, the SLOD problem is formulated, aiming to improve the overall mass characteristics of satellite. To explore the layout design space globally, the DE algorithm is utilized as the main framework of the proposed hybrid algorithm. Then in order to improve the local exploitation capability and algorithm robustness, sequential quadratic programming (SQP), as a gradient-based method, is combined with DE in two unique ways, comprising two types of hybrid algorithm. In the first type of hybrid algorithm (denoted by DESQP), SQP is performed when iteration process of DE has finished and only the final solution of DE is used as the initial point of SQP, the purpose of which is to locate the most promising area of optimum with DE first and then make a rapid exploitation around the quasi-optimum. In the second type of hybrid algorithm (denoted by DESQPDE), SQP is performed in the specific iteration of DE and all the current-generation population individuals are used as the initial points, the purpose of which is to accelerate the evolution process while holding the diversity of the population and to enhance the robustness. Finally, the efficacy and robustness of the proposed hybrid algorithms are compared with classical DE and also validated by two three-dimensional satellite layout cases with 14 and 40 components, respectively.

To properly define satellite lifetime so as to maximize economic net benefit, the lifetime optimization method is studied in this paper based on discrete cross entropy (CE) method. Firstly, the influences of lifetime on satellite system design, cost and revenue are studied and the disciplinary models are developed. Then the lifetime optimization problem is formulated, which is a typical discrete optimization problem with the discrete lifetime measured in years and the combination of the subsystem components as design variables. To efficiently solve this problem, the CE method is used as the optimization solver, and a convolution based smoothing strategy is proposed to enhance the space exploration capability and algorithm robustness. The efficacy of the proposed method is demonstrated in a 19 dimensional satellite lifetime design problem, which also verifies the importance of lifetime optimization in enhancing the satellite economic benefit.

No doubt that the proper design of satellite system reliability is one of the most important issues in satellite system engineering. In order to optimize the satellite reliability, the reliability optimization method is studied in this paper based on compression algorithm, Bayesian Network (BN) and Particle Swarm Optimization (PSO). Firstly, the improved compression inference algorithm (ICIA) is proposed to model the BN reliability model of multistate system based on the existing compression and inference algorithms. Then, the reliability optimization problem is formulated, which is a typical discrete optimization problem with the discrete reliability measured in the number of each component's units as design variables. The process of optimization, based on the BN reliability model of multistate satellite system will be achieved by PSO. The efficacy of the proposed method is demonstrated in a microsatellite system reliability optimization, which verifies the importance of satellite reliability optimization in decreasing the cost of satellite development.

Multidisciplinary design optimization (MDO) under uncertainty is increasingly being recognized in improving the performance, safety, and reliability of aerospace vehicles. However, the solution process is still challenging, especially in multi-objective optimizations. In this study, a multi-objective alliance algorithm (MOAA) is employed and corresponding computational heuristics are presented, including system decoupling strategy, active subspaces, and surrogate model. Both reliability-based design optimization (RBDO) and robust design optimization (RDO) are considered to prove the efficacy of the proposed approach, which is exemplified by the conceptual design of a small satellite mission for the Moon imaging. Among multiple system uncertainties and closely coupled disciplines, the approach exhibits high effectiveness and strong adaptability at considerably less cost, thus providing a potential approach to solving widely exiting MDO problems of aerospace vehicles.

In this paper, the problem of relative motion control of spacecraft close-range rendezvous without collision on elliptical orbit is considered. An autonomous amalgamated control algorithm is presented to deal with the nonlinear dynamics problem with external disturbances. This new control scheme combines the efficiency of the optimal sliding mode control (OSMC) (used for attraction towards goal position) and the collision avoidance capability of the artificial potential function (APF) method (used for repulsion from moving obstacle). The nonlinear optimal control strategy is based on infinite-horizon state-dependent Riccatic equation (SDRE). For ensuring robustness of the optimal controller in presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The APF controller takes velocity of both the chaser and obstacle into consideration. Using the Lyapunov stable theory, the stability of the closed-loop system is guaranteed. Numerical simulation results are presented to verify the effectiveness and capability of the proposed control scheme.

Surrogate modeling is a common method for computationally intensive engineering design optimization problems. For lack of prior knowledge, it is difficult to decide which surrogate is more suitable for approximation. In order to take full advantage of multiple surrogates, ensembles of surrogate models have been gradually focused on. However, the current ensemble methods do not consider the relation between weights and error variation of component surrogates, which is helpful for improving the prediction accuracy. In this paper, a novel point-wise weighted ensemble approach of surrogate models is proposed, which combines error classification and nearest neighbour choosing into point-wise weights computing. First, the leave-one-out cross validation errors of each component surrogate are classified into several error levels based on unsupervised learning. Second, nearest neighbour training points of each test point are selected according to distance sorting. Considering local error of each test point, only nearest neighbour training points which achieve a user-defined error level of each component surrogate are selected. Finally, based on the above steps, the point-wise weights of each test point are computed for the component surrogates. Experiments show that the proposed ensemble method outperforms the state-of-the-art methods in three benchmark function examples.

A hydrodynamic system with pressurized tank is modeled for flow meter calibration which has excellent advantages in laboratory application over the water-tower system, broadly used in the industrial calibration application; however, such system meets challenges of fluid flow stabilization and pressure noise absorption. While previous approach to deal with such problems is based on complicated mechanical method which is hard to master, present paper contributes a new novel method based on integral sliding mode control strategy to design a feedback controller to asymptotically and robustly stabilize the hydraulic pressure even within the system uncertainty and, moreover, hydraulic accumulator is investigated to absorb the pressure pulsation by unsteady fluid flow as well as the chattering of the sliding mode control. Simulation shows the validity and robustness of the proposed controller within the system uncertainty as well as the accumulator absorption of pressure pulsation. Moreover, analysis of the sliding mode parameter is addressed in this paper which is important in control design.

Radial basis function neural networks (RBFNNs) are widely used in nonlinear function approximation. One of the challenges in RBFNN modeling is determining how to effectively optimize width parameters to improve approximation accuracy. To solve this problem, a width optimization method, concurrent subspace width optimization (CSWO), is proposed based on a decomposition and coordination strategy. This method decomposes the large-scale width optimization problem into several subspace optimization (SSO) problems, each of which has a single optimization variable and smaller training and validation data sets so as to greatly simplify optimization complexity. These SSOs can be solved concurrently, thus computational time can be effectively reduced. With top-level system coordination, the optimization of SSOs can converge to a consistent optimum, which is equivalent to the optimum of the original width optimization problem. The proposed method is tested with four mathematical examples and one practical engineering approximation problem. The results demonstrate the efficiency and robustness of CSWO in optimizing width parameters over the traditional width optimization methods.

The design of radial basis function widths of Radial Basis Function Neural Network (RBFNN) is thoroughly studied in this paper. Firstly, the influence of the widths on performance of RBFNN is illustrated with three simple function approximation experiments. Based on the conclusions drawn from the experiments, we find that two key factors including the spatial distribution of the training data set and the nonlinearity of the function should be considered in the width design. We propose to use Euclidean distances between center nodes and the second derivative of function to measure these two factors respectively. Secondly, a two step method is proposed to design the widths based on the information about the aforementioned two key factors obtained from comprehensive analysis of the given training data set. In the first step the data set spatial distribution features are analyzed according to the Euclidean distances between the data points, and the second derivative of each center node is estimated with finite difference approximation method. Based on the analysis an initial design of the widths is given with a heuristic equation. In the second step optimization techniques are used to optimize the widths which can effectively find the optimum with the good initial baseline. Thirdly, one mathematical example is taken to verify the efficiency of the proposed method, and followed by conclusions.