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Clean-Label Attack on Face Authentication Systems Through Rolling Shutter Mechanism

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 36-40页

作者： Wang, Yufei Li, Haoliang Zhang, Liepiao Hu, Yongjian Kot, Alex C. Nanyang Technol Univ Rapid Rich Object Search ROSE Lab Singapore 639798 Singapore City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China GRGTally vis IT Co Ltd Guangzhou 510663 Peoples R China South China Univ Technol Guangzhou 510640 Peoples R China

We introduce a novel clean-label black-box face presentation attack on face authentication systems, i.e., face recognition and verification systems, under mild conditions. Different from other clean-label attacks which require inserting complicated or intensity patterns after the image-capturing phase, our designed pattern can be automatically inserted during the exposure by utilizing the rolling shutter mechanism and modulating environment LEDs in a specialized waveform. This method provides a potential way to conduct backdoor attacks in the physical domain. Additionally, we propose an optimization strategy based on evolutionary computing to optimize the parameters of the stripe patterns, enhancing the attack success rate. The experimental results on several face recognition models and face verification services provided by the leading technology companies demonstrate the effectiveness of our attack method. Our study reveals a new attack applicable in the physical world, highlighting significant security concerns for existing face recognition, verification, and face anti-spoofing techniques.

关键词： Face recognition Light emitting diodes Image color analysis Faces Face detection Data models Computational modeling Closed box Web sites Video on demand Clean-label black-box attack evolutionary computing face recognition/verification rolling shutter

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BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge

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IEEE ACCESS 2024年 12卷 145893-145919页

作者： Yannibelli, Virginia Hirsch, Matias Toloza, Juan Majchrzak, Tim A. Gronli, Tor-Morten Zunino, Alejandro Mateos, Cristian ISISTAN UNICEN CONICET RA-7000 Tandil Buenos Aires Argentina Univ Agder Dept Informat Syst N-4630 Kristiansand Norway Kristiania Univ Coll Sch Econ Innovat & Technol N-0107 Oslo Norway

Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each r(i), so that the smartphones have the required start battery levels pre-configured for r(i), which requires discharging or charging smartphones. This leads to a number of e = r & lowast;g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS's performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.

关键词： Smart phones Batteries Genetic algorithms Software Urban areas Mathematical models Traveling salesman problems Software algorithms Load modeling Load management Edge computing smartphone profiling benchmarking evolutionary computing

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Multiobserver Trajectories Optimization Based on APSO Algorithm in Underwater BOT System

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IEEE SENSORS JOURNAL 2025年第1期25卷 1054-1067页

作者： Luo, Naifu Wang, Hongjian Cao, Xu Zhang, Kai Ren, Jingfei Li, Chengfeng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430205 Peoples R China

Aiming at solving the problem of underwater passive bearing-only tracking (BOT) system, it is difficult to judge the rule of bearing change, which leads to the slow convergence or low tracking accuracy with the predetermined unmanned underwater vehicle's (UUV) route. In this article, the observer's model is based on the UUV's kinematic model. Then, the underwater multiobserver BOT system is designed. To improve the collaborative tracking performance, the recursive form of Fisher information matrix (FIM) is deduced and introduced as the optimal index for multi-UUV trajectories' planning. From the perspective of UUV's kinematic properties, the mathematical algorithm based on gradient method is proposed. In addition, the adaptive particle swarm optimization (APSO) algorithm is designed with four operators which could balance the process of exploration and exploitation adaptively. Finally, the Monte Carlo simulation proves the effectiveness of these two proposed algorithms. Sea trial demonstrates the robustness and adaptability. The tracking performance has been improved through APSO algorithm even with small-angle maneuvering.

关键词： Observers Target tracking Observability Chatbots Sensors Cost function Trajectory optimization Radar tracking Sonar Mathematical models Bearing-only evolutionary computing observers' trajectories optimization target tracking

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An adaptive pyramid PSO for high-dimensional feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 257卷

作者： Jin, Xiao Wei, Bo Deng, Li Yang, Shanshan Zheng, Junbao Wang, Feng Zhejiang Sci Tech Univ Sch Comp Sci & Technol Sch Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Longgang Res Inst Longgang 325000 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Feature selection (FS) is an important data pre-processing method in machine learning tasks. However, FS becomes an NP-hard combinatorial optimization problem due to the huge search space of high-dimensional datasets and the complex interaction among features. As a simple implementation algorithm, particle swarm optimization (PSO) has been employed for dealing with FS problems. At the same time, it still suffers from issues such as the premature convergence and falling into local optimum. In this paper, an adaptive pyramid PSO (APPSO) is proposed for FS on dataset classification problems. In APPSO, a weighted initialization strategy based on correlation and the cubic chaotic map is employed to generate particles in the initial stage, aiming to improve the diversity of a population. Then, an adaptive constrained updating strategy, based on the pyramid structure, is proposed to enhance the exploration ability of the population. Moreover, a new dynamical flip strategy (DFS), which combines feature correlation and occurrence frequencies of features, is proposed to improve the exploitation ability of the population. The proposed APPSO is compared with 8 representative wrapper-based FS methods. Experimental results show that APPSO significantly outperforms these competitors on all 18 datasets.

关键词： Particle swarm optimization evolutionary computing Swarm intelligence Wrapper method Feature selection

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COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

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COGNITIVE COMPUTATION 2024年第4期16卷 1765-1777页

作者： Singh, Asu Kumar Kumar, Anupam Mahmud, Mufti Kaiser, M. Shamim Kishore, Akshat Maharaja Agrasen Inst Technol CSE Dept Delhi India Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Nottingham Trent Univ Med Technol Innovat Facil Nottingham NG11 8NS England Jahangirnagar Univ Inst Informat Technol Dhaka 1342 Bangladesh

A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

关键词： Computer-aided detection system Feature reduction evolutionary computing Social group optimization

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Joint Uplink - Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4032-4048页

作者： Tekgul, Ezgi Novlan, Thomas Akoum, Salam Andrews, Jeffrey G. Univ Texas Austin Wireless Networking & Commun Grp 6GUT Austin TX 78712 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA AT&T Labs Austin TX 78759 USA

We propose a novel framework for optimizing antenna parameter settings in a heterogeneous cellular network. We formulate an optimization problem for both coverage and capacity- in both the downlink (DL) and uplink (UL)- which configures the tilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across the network. The novel data-driven framework proposed for this nonconvex problem, inspired by Bayesian optimization (BO) and differential evolution algorithms, is sample-efficient and converges quickly, while being scalable to large networks. By jointly optimizing DL and UL performance, we take into account the different signal power and interference characteristics of these two links, allowing a graceful trade-off between coverage and capacity in each one. Our experiments on a state-of-the-art 5G NR cellular system-level simulator developed by AT&T Labs show that the proposed algorithm consistently and significantly outperforms the 3GPP default settings, random search, and conventional BO. In one realistic setting, and compared to conventional BO, our approach increases the average sum-log-rate by over 60% while decreasing the outage probability by over 80%. Compared to the 3GPP default settings, the gains from our approach are considerably larger. The results also indicate that the practically important combination of DL throughput and UL coverage can be greatly improved by joint UL-DL optimization.

关键词： Coverage and capacity optimization Bayesian optimization evolutionary computing machine learning antenna parameter optimization uplink (UL) downlink (DL)

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Nature-based metaheuristic optimisation techniques for band selection in drone-based hyperspectral images for plant-level crop classification

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年第2期39卷 599-611页

作者： Sarma, Anagha S. Nidamanuri, Rama Rao Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram 695547 Kerala India

Hyperspectral imagery-based remote sensing has emerged as a major contributing factor for precision agriculture. Drone-based hyperspectral imagery provides high resolution data for plant-level analysis of agriculture fields, making it possible to discriminate crops with subtle differences in their spectral signatures. However, the high dimensionality and correlated bands in the hyperspectral imagery make data processing challenging in an operational environment. Feature selection plays a vital role in reducing the dimensionality of hyperspectral data without losing spectral integrity. Though evolutionary computing-based optimal feature selection has been applied for hyperspectral data at a broader level, their applications are not explored for crop classification in drone-based hyperspectral imagery, especially at plant-level. This work has evaluated several nature-inspired metaheuristic optimisation algorithms to classify drone hyperspectral imagery at different flying altitudes for crop classification at plant level. We acquired ultra-high spatial resolution hyperspectral imagery from a drone over research farms of the University of Agricultural Sciences, GKVK, Bengaluru, India. From the perspective of studying drone-based hyperspectral imaging for plant-level mapping and the interference of background introduced by varying flying height, this work is the first of its kind to study the impact of the optimisation techniques for hyperspectral band reduction on drone data acquired at different heights. The models are trained on a dataset captured at the height of 30 m and further tested on images acquired over 25 m and 40 m. The outcomes suggest the requirement of only selected bands from certain wavelengths for achieving classification accuracy as compared with the original set of bands. The results also emphasise the effective application of evolutionary computing-based optimisation techniques for hyperspectral band reduction, irrespective of the height of the data

关键词： Optimisation Band selection Metaheuristic algorithms evolutionary computing Hyperspectral imaging

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EvoRecSys: evolutionary framework for health and well-being recommender systems

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USER MODELING AND USER-ADAPTED INTERACTION 2022年第5期32卷 883-921页

作者： Alcaraz-Herrera, Hugo Cartlidge, John Toumpakari, Zoi Western, Max Palomares, Ivan Univ Bristol Dept Comp Sci Bristol Avon England Univ Bristol Sch Policy Studies Bristol Avon England Univ Bath Dept Hlth Bath Avon England Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 70101 Taiwan Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada Spain

In recent years, recommender systems have been employed in domains like e-commerce, tourism, and multimedia streaming, where personalising users' experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users' study.

关键词： Recommender systems evolutionary computing Genetic algorithms Food recommendation Physical activity recommendation Well-being

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Towards Planning Urban Air Mobility (UAM) Landing Trajectories in Emergencies

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2025年第1期111卷 1-23页

作者： Pinto Neto, Euclides Carlos Baum, Derick de Almeida Jr, Jorge Rady Camargo Jr, Joao Batista Cugnasca, Paulo Sergio Univ Sao Paulo Sch Enginering Safety Anal Grp GAS 380 Prof Luciano Gualberto Ave BR-05508010 Sao Paulo SP Brazil

Ground transportation in dense urban environments has been facing challenges for many years (e.g., congestion and resilience) and the problem of congestion in urban environments will become more significant with the growth of populations and urbanization. In the past few years, the industry and the scientific communities have invested resources towards creating new ideas to improve urban transportation performance, such as the Urban Air Mobility (UAM) concept. Therefore, emergencies are considered a pivotal aspect to be managed appropriately to ensure safe UAM operations. Although normal operations represent a challenge nowadays (e.g., performance and social acceptance), emergencies are even more challenging due to the safety-critical risks. Thereupon, the main goal of this research is to propose the Landing Trajectory Planner for Emergencies in UAM Operations (LTPE), using Parallel Metaheuristics and considering autonomous vehicles' presence. This trajectory planning method aims to design landing trajectories for multiple Electrical Vertical Take-off and Landing (eVTOL) vehicles in normal conditions and in emergencies. LTPE considers different eVTOL configurations in piloting systems (i.e., piloted vehicles, remotely piloted vehicles, and fully autonomous vehicles) as well as different vehicle priorities. Three landing modes are used for vehicles in emergency conditions: (i) land at the originally designed skyport;(ii) land at the nearest skyport;and (iii) land on the ground) and all metaheuristics include an early stopping feature. The experiments showed that LTPE can propose safe and efficient solutions for several scenarios with a short response time.

关键词： Urban air mobility eVTOL UAS Artificial intelligence evolutionary computing Trajectory planning Airspace Safety

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Industrial defect detection and location based on greedy particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2025年第3期40卷 214-221页

作者： Tang, Yaorui Yang, Bo Peng, Hong Luo, Xiaohui Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

Particle swarm optimization algorithm is a branch of evolutionary computing and can search for a better solution in a given feature space. This paper introduces the particle swarm optimization algorithm and greedy strategy for the defect detection and location of industrial components and then proposes a greedy particle swarm optimization algorithm. This paper employs sparse random projection to map the vast high-dimensional information to the low-dimensional space while maintaining the relative distance between the data. This employment helps to accelerate the training and prediction speed of the model and remove some unimportant features or noise. This algorithm first adopts particle swarm optimization (PSO) to initialize the cluster centers in an effort to minimize the maximum distance between all unlabeled data points and these centers. The algorithm then utilizes the greedy strategy to select a batch of data points to represent the corresponding features of the normal image, thereby improving the coverage of the model to the data. Experiments have shown that the results of most categories of data sets are close to or better than the current existing methods, especially in defect detection. In terms of defect localization for object categories, our method achieves a pixel-level anomaly localization index (AUROC) of 98.3% on the MVTec AD dataset. [GRAPHICS]

关键词： evolutionary computing optimization algorithm feature extraction greedy strategy industrial defect detection and location

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