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Security Issues and Defensive Approaches in Deep Learning Frameworks

Tsinghua science and Technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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A control Theoretic Study on Omnidirectional MAVs with Minimum Number of Actuators and No Internal Forces at Any Orientation

A Control Theoretic Study on Omnidirectional MAVs with Minim...

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IEEE Conference on Decision and control

作者： Ahmed Ali Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF’s, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations.

关键词： Three-dimensional displays Propellers Dynamics controllability Regulation Steady-state Feedback linearization control theory Closed loop systems Vehicle dynamics

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Degree of Spiking Neural P Systems Without Delay

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Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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An Experimentally Validated Model of the Propeller Force Accounting for Cross Influences on Multi-Rotor Aerial Systems

An Experimentally Validated Model of the Propeller Force Acc...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Barbara Bazzana Ralph Brantjes Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this paper, we propose a model for the thrust coefficient of propellers that can take into account cross-influence between adjacent propellers. The aerodynamic interaction between propellers in multirotor aerial vehicles reduces the thrust they can produce. The influence between propellers depends on their relative positioning and orientation, which are taken into account by the proposed model. It is validated on measurements collected by a force sensor mounted on a propeller for different configurations of the adjacent propellers in a support structure. In this work, we focus on configurations with small relative orientations. Results show that the proposed model outperforms the traditional constant model in terms of thrust prediction on the data we collected, and it performs better than other models with fewer parameters, being the only one with less than 10% maximum percentage error.

关键词： Training Adaptation models Torque Propellers Atmospheric modeling Force Velocity control

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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ELOFS: An Extensible Low-Overhead Flash File System for Resource-Scarce Embedded Devices

ELOFS: An Extensible Low-Overhead Flash File System for Reso...

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作者： Zhang, Runyu Liu, Duo Chen, Xianzhang She, Xiongxiong Yang, Chaoshu Tan, Yujuan Shen, Zhaoyan Shao, Zili Qiao, Lei Chongqing400044 China Chongqing University College of Computer Science Chongqing400044 China Shandong University School of Computer Science and Technology Qingdao266237 China The Chinese University of Hong Kong Department of Computer Science and Engineering NT 999077 Hong Kong Beijing Institute of Control Engineering Beijing100190 China

Emerging applications like machine learning in embedded devices (e.g., satellites and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-scale flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for two reasons. First, prior arts store data structures of file systems either in flash or in main memory, which severely magnifies the scarcity of computing and memory resources. Moreover, the fine-grained metadata management in the existing embedded file systems induces significant energy consumption for large-capacity storage. In this paper, we propose a novel embedded file system, ELOFS, to tackle the above issues and manage large-capacity NAND flash on resource-scarce devices. ELOFS is made efficient through three novel techniques. First, we redefine the space management granularity and streamline the metadata to speed up the mounting performance. In addition, we design hybrid file structures to adapt dissimilar access patterns of embedded devices. Furthermore, ELOFS provides opportunities for in-depth cooperation with application-specific systems. We implement ELOFS with Memory Technology Device (MTD) interfaces, and the experimental results show that ELOFS outperforms YAFFS and UBIFS in terms of write, read, and deletions with orders of magnitude reductions on memory footprint and mounting time. © 1968-2012 IEEE.

关键词： Metadata

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Hong, Tzung-Pei Li, Jia-Xiang Tsai, Yu-Chuan Huang, Wei-Ming National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung Taiwan National Sun Yat-Sen University Department of Computer Science and Engineering Kaohsiung Taiwan National University of Kaohsiung Library and Information Center Kaohsiung Taiwan China Steel Inc Department of Electrical and Control Kaohsiung Taiwan

ISBN: (纸本)9781665480451

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets. © 2022 IEEE.

关键词： Data mining

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Distributionally Robust control for Chance-Constrained Signal Temporal Logic Specifications 63

Distributionally Robust Control for Chance-Constrained Signa...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. Soudjani, Sadegh Max Planck Institute for Software Systems Kaiserslautern Germany Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Southern California Viterbi School of Engineering Thomas Lord Department of Computer Science Los Angeles United States

ISBN: (纸本)9798350316339

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method. © 2024 IEEE.

关键词： Stochastic systems

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Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection

arXiv

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arXiv 2023年

作者： Flaborea, Alessandro di Melendugno, Guido Maria D'Amely D'Arrigo, Stefano Sterpa, Marco Aurelio Sampieri, Alessio Galasso, Fabio Department of Computer Science Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model that encodes skeletal human motion by a graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Video Anomaly Detection. We propose three latent spaces: the commonlyadopted Euclidean and the novel spherical and hyperbolic. All variants outperform the state-of-the-art on the most recent UBnormal dataset, for which we contribute a human-related version with annotated skeletons. COSKAD sets a new state-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK Avenue, with performance comparable to video-based methods. Source code and dataset will be released upon acceptance. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph embeddings

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