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Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

computers, Materials & Continua 2024年第1期78卷 899-914页

作者： Chengsheng Yuan Baojie Cui Zhili Zhou Xinting Li Qingming Jonathan Wu Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of International Relations National University of Defense TechnologyNanjing210039China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable ***,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake *** of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial *** addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual *** response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for *** ridge texture area within the fingerprint image has been identified and designated as the region for perturbation ***,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient ***,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack *** results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

关键词： FLD adversarial attacks adversarial examples gradient optimization transferability

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Vehicle State Monitoring and Fault Detection System for Unmanned Ground Vehicles (UGV) using Markov Models 15

Vehicle State Monitoring and Fault Detection System for Unma...

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15th Annual Conference of the Prognostics and Health Management Society, PHM 2023

作者： Vadnerkar, Kalpit Pisu, Pierluigi Clemson University Holcombe Department of Electrical and Computer Engineering ClemsonSC29634 United States Clemson University International Center for Automotive Research GreenvilleSC29607 United States

ISBN: (纸本)9781936263059

This research presents a novel fault detection and diagnostics system for unmanned ground vehicles (UGVs) by combining Markov models representing the vehicle's navigation, kinematic behavior, and vehicle dynamics systems. Existing studies do not specifically address the challenges related to UGVs and their complex subsystems or the incorporation of weather and environmental condition data. The proposed system leverages environmental and weather condition data to monitor the UGV's state and detect anomalies in its behavior. By predicting the probability of faults such as collisions, sensor damage, and other malfunctions, the system aims to enhance the safety, reliability, and performance of UGVs. The research will demonstrate the effectiveness of the proposed methodology through case studies and performance evaluation, highlighting its potential application in various real-world scenarios. This work contributes to the ongoing research in prognostics and health management, particularly for autonomous systems, by providing a new approach to fault detection and diagnostics in UGVs. © 2023 Prognostics and Health Management Society. All rights reserved.

关键词： Ground vehicles

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Deep Learning Implementation for Pineapple Sweetness Classification 1st

Deep Learning Implementation for Pineapple Sweetness Classi...

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1st International Conference on Data Science and Artificial Intelligence, DSAI 2023

作者： Muchallil, Sayed Roza, Silvia Al-Assad, Muhammad Hafez Candra, Yudi Fitria, Maya Dawood, Rahmad Department of Electrical and Computer Engineering Universitas Syiah Kuala Aceh Banda Aceh23111 Indonesia Telematics Research Center Universitas Syiah Kuala Aceh Banda Aceh23111 Indonesia

ISBN: (纸本)9789819979684

In 2022, Indonesia produced roughly 3.203.775 metric tons of pineapples. The sweetness of pineapples is an important factor in determining their selling price. Typically, a pineapple’s sweetness is determined manually, which is time-consuming and prone to human error, resulting in a lower selling price and pineapple waste due to unsold fruit. To address this issue, we developed a deep learning-based algorithm for identifying the sweetness of pineapples based on the hypothesis that a pineapple’s sweetness is closely related to its color and maturity. To train the algorithm, we took 872 images of freshly harvested Smooth Cayenne pineapples and measured their sweetness with a Brix measuring device. We use two algorithms, EffisientNetB4 and RestNet-50, both with a learning rate of 0.000001, to differentiate between sweet and not-so-sweet pineapple. Both algorithms detected pineapple sweetness with the same accuracy of 84.09%. However, RestNet50 had a greater loss than EffisintNetB4. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep learning

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Optical Fiber Curvature Sensor With High Sensitivity and a Broad Dynamic Range Enabled by Machine Learning

Optical Fiber Curvature Sensor With High Sensitivity and a B...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Zhu, Chen Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

An optical fiber curvature sensor based on a no-core fiber (NCF) cascaded with a hollow-core fiber (HCF), realizing simultaneously high sensitivity and a broad dynamic range with the assistance of machine learning ana... 详细信息

ISBN: (纸本)9781957171296

关键词： Curvature sensors Fiber Bragg gratings Fiber optic sensors Long period fiber gratings Machine learning Optical fibers

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The Analysis of Hydrogen and Nitrogen Mediums for VLC-Based Downhole Gas Pipe Monitoring System

The Analysis of Hydrogen and Nitrogen Mediums for VLC-Based ...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Alaca, Ozgur Tokgoz, Sezer C. Retnanto, Albertus Miller, Scott L. Qaraqe, Khalid A. Texas A&m University Department of Electrical and Computer Engineering College StationTX United States Qualcomm Research Center Qualcomm Technologies Inc. San DiegoCA United States Texas A&m University at Qatar Department of Petroleum Engineering Doha Qatar Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350337822

Downhole gas pipe monitoring systems have an essential role in the extraction and monitoring processes of oil and gas. Since the most critical part of these systems is real-time data transmission, a novel transmission system is required to meet increasing bandwidth and capacity prerequisites. Therefore, visible light communication (VLC) becomes a significant emerging technology for downhole gas pipe monitoring systems in terms of high bandwidth and data rate. Following this direction, the VLC-based downhole gas pipe monitoring system is investigated by considering different modulation schemes and mediums. Specifically, a carbon steel-coated gas pipe channel is established with a transmitting light source and a photodetector to simulate the real field conditions of the VLC-based gas pipe monitoring system. Also, the well-known ACO-OFDM and OKK modulation schemes are employed and compared under real-world conditions. On the other hand, this study investigates for the first time the use of hydrogen gas medium, which is commonly employed to make lighter products from heavy petroleum fractions. Then, based on practical considerations, the performance of the VLC-based system is analyzed under different gases including nitrogen and hydrogen. Hence, for the performance metric, the bit-error rate is utilized and presented by considering the impact of internal pipe pressure and the circulating pump. As for the channel characterization of the hydrogen and nitrogen mediums, the channel impulse response is obtained and used to calculate the channel DC gain and path loss. Results demonstrate that the proposed VLC-based system achieves significant performance and is slightly affected by channel modifications. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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DNN-Based Velocity Estimator Using Inertial Sensor for Robot Localization

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IFAC-PapersOnLine 2023年第2期56卷 5027-5032页

作者： Chul-hong Kim Dong-il “Dan” Cho Department of Electrical and Computer Engineering and Automation and Systems Research Institute (ASRI) Seoul National University Seoul Korea Inter-university Semiconductor Research Center (ISRC) Seoul National University Seoul Korea

This paper presents an inertial sensor-based localization method using a deep neural network (DNN)-based velocity estimator. Among various sensors commonly used in localization, inertial sensors are less affected by the surrounding environment. The inertial sensor consists of an accelerometer and a gyroscope, which can estimate the poses of a robot. However, inertial sensors need to be used with other sensors for localization because inherent large drift errors are difficult to prevent. To overcome this problem, a DNN-based velocity estimator that reduces the position error by learning the data pattern of the inertial sensor and by limiting the range of the estimated velocity is proposed. The DNN-based velocity estimator comprises a convolutional neural network, a fully connected layer, and a smoothing filter. To limit the range of the estimated velocity, the range of velocity is divided by the total number of classes, and the divided range is assigned to each class. The relationship between consecutive classes is learned using a smoothing filter. Dataset-based experiments are performed to train the DNN-based velocity estimator and to evaluate the performance of the proposed localization. The experimental result shows that the proposed localization reduces the position errors by 99% compared to integration-based localization and by 91% compared to extended Kalman filter-based localization.

关键词： Localization Mobile robot Inertial sensor Deep learning Kalman filter

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Generative Neural Net for Spatial Concept-To-Image

Generative Neural Net for Spatial Concept-To-Image

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Young, Brendan Anderson, Derek T. Keller, James M. Petry, Fred Michael, Chris J. University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO United States Stennis Space Center MS U.S. Naval Research Laboratory United States

ISBN: (纸本)9798350359527

Numerous artificial intelligence (AI) approaches, such as generative AI (GAI), large language models (LLM) and text-To-image networks, necessitate spatial intelligence for effective operation. Yet, a prevailing ideology embedded in contemporary solutions is their inclination to strictly adhere to a data-driven approach when addressing spatial learning. This viewpoint leads to opaque solutions, aka "black boxes", and it presupposes that the intricacies of spatial reasoning will effortlessly surface as an inherent byproduct of extensive data exposure. This simplistic reliance on data overlooks the wealth of established psychology and mathematics governing spatial relations and their inherent uncertainties. In this article, we delve into neural networks and learning strategies for incorporating well-established spatial relations mathematics into an effective and explainable GAI. Specifically, we showcase how to translate abstract concepts, expressed as sets of spatial relations, into visual imagery. This resulting imagery can be immediately used or passed on as a spatial prior to a larger and more sophisticated GAI. We present two synthetic use cases wherein spatial concepts materialize as intricate arrangements of multi-part colored geometric primitives, undergoing a spectrum of diverse planar affine transformations. Our exploration is categorized into two dimensions: discerning spatial placements (aka the "where") and shape depiction (aka the "what"). Overall, we've noted a strength in our proposed neural networks in discerning spatial placements. However, while our nets also exhibit a competence in determining "what"to depict, we recognize the necessity for additional fine-Tuning to achieving visual imagery consistent with state-of-The-Art works like DALL-E and stable diffusion. © 2023 IEEE.

关键词： Abstracting

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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

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IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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Impact of Data Variety on Physics-informed Neural Network Lens Design 8

Impact of Data Variety on Physics-informed Neural Network Le...

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Advanced Optics for Imaging Applications: UV through LWIR VIII 2023

作者： Lindsay, Marshall B. Kovaleski, Scott D. Varner, Andy G. Veal, Charlie Weber, Justice Anderson, Derek T. Price, Stanton R. Price, Steven R. Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (数字)9781510661752

ISBN: (纸本)9781510661745

The application of neural networks in optical component design has witnessed rapid growth in recent years. This design framework encodes the numerical simulation of optical wave propagation and material wave modulation directly within neural network layers, enabling the optimization of physical quantities, such as transmissivity values of diffractive optical elements, through gradient descent and backpropagation algorithms. Physics-informed neural networks have been employed in designing diffractive deep neural networks, optimizing holograms for near-eye displays and creating multi-objective traditional optics. However, there remains a lack of evaluation for training parameters, and discrete sampling considerations are often overlooked. To address these gaps, this study examines the impact of dataset variety on physics-informed neural networks in optimizing lenses that either satisfy or violate the Nyquist sampling criteria. Results show that increased data variety enhances optimized lens performance across all cases. Optimized lenses demonstrate improved imaging performance by reducing diffraction orders present in aliased analytical lenses. Moreover, we reveal that low data variety leads to overfit lenses that function as selective imagers, providing valuable insights for future lens design and optimization. © 2023 SPIE.

关键词： Optimization

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