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Boosting Black-Box Adversarial Attacks with Meta Learning

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arXiv 2022年

作者： Fu, Junjie Sun, Jian Wang, Gang The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have been proposed to in the literature. However, those methods usually suffer from low success rates and large query counts, which cannot fully satisfy practical purposes. In this paper, we propose a hybrid attack method which trains meta adversarial perturbations (MAPs) on surrogate models and performs black-box attacks by estimating gradients of the models. Our method uses the meta adversarial perturbation as an initialization and subsequently trains any black-box attack method for several epochs. Furthermore, the MAPs enjoy favorable transferability and universality, in the sense that they can be employed to boost performance of other black-box adversarial attack methods. Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Data-Driven Control of Distributed Event-Triggered Network systems

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven ETS in cutting off data transmissions and the co-design procedure. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Event-triggered Consensus Control of Heterogeneous Multi-agent systems: Model- and Data-based analysis

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

An inexact primal-dual method with correction step for a saddle point problem in image debluring

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arXiv 2021年

作者： Fang, Changjie Hu, Liliang Chen, Shenglan Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Science Chongqing University of Posts and Telecommunications Chongqing 400065 China

In this paper, we present an inexact primal-dual method with correction step for a saddle point problem by introducing the notations of inexact extended proximal operators with symmetric positive definit ematrix D. Relaxing requirement on primal-dual step sizes, we prove the convergence of the proposed method. We also establish the O(1/N) convergence rate of our method in the ergodic sense. Moreover, we apply our method to solve TV-L1 image deblurring problems. Numerical simulation results illustrate the efficiency of our method. © 2021, CC BY-NC-SA.

关键词： Image enhancement

A Ranking Model for Intuitionistic Fuzzy Preference Relation Under Uncertainty for Targeted Poverty 15th

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A Ranking Model for Intuitionistic Fuzzy Preference Relation...

15th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Yu, Dongxu Xian, Sidong School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783030324551

Intuitionistic fuzzy preference relation (IFPR) is a suitable tool to present preference information and hesitation for decision maker among poverty alleviation candidates. This paper aims to provide an effective decision method for helping decision maker accurately to obtain priority weights by using IFPRs to represent the proportion of importance from candidates and rank them. Firstly, a fractional interactive minimizing deviation programming model based on multiplicative consistency-based method to derive weight vectors of alternatives from IFPRs based on multiplicative consistency is presented. Specifically, for any IFPR, by minimizing its absolute deviation from the corresponding consistent IFPR, the weight vectors are generated. Then, a new score function based on Euclidean distance is proposed. This defuzzification value is computed by constructing the idea of closest to ideal solution based on distance measure and ranks alternatives. Subsequently, a numerical example about targeted poverty alleviation selection is given to demonstrate the methods effectiveness. © Springer Nature Switzerland AG, 2020.

关键词： decision making

Finite-time tracking consensus in probability for multi-agent system with noises and time delay

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Finite-time tracking consensus in probability for multi-agen...

International Conference on Information Science and Technology (ICIST)

作者： Mingyan Tuo Fenglan Sun Feng Wang Hao Li Key Lab of Intelligent Analysis and Decision on Complex Systems School of Science Chongqing University of Posts and Telecommunications Chongqing China Key Lab of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665412667;9781665429412

This paper studies the finite-time tracking consensus in probability of multi-agent systems with noises and time delay. By using the method of pinning control and observer, the system both with and without the leader as the root of a spanning tree is considered. According to algebraic graph theory, stochastic Lyapunov stability theory and control theory, sufficient conditions for the tracking consensus are given. Numerical simulations are given to verify the effectiveness of the main results.

关键词： Sufficient conditions Information science Protocols Delay effects Observers Numerical simulation Graph theory

Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI systems

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Data-driven Self-triggered Control via Trajectory Prediction

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arXiv 2022年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples. © 2022, CC BY.

关键词： Model predictive control

Self-triggered Resilient Stabilization of Linear systems with Quantized Output

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arXiv 2023年

作者： Liu, Wenjie Wakaiki, Masashi Sun, Jian Wang, Gang Chen, Jie Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chonqing401120 China Graduate School of System Informatics Kobe University Hyogo657-8501 Japan Department of Control Science and Engineering Tongji University Shanghai201804 China

This paper studies the problem of stabilizing a self-triggered control system with quantized output. Employing a standard observer-based state feedback control law, a self-triggering mechanism that dictates the next sampling time based on quantized output is co-developed with an output encoding scheme. If, in addition, the transmission protocols at the controller-to-actuator (C-A) and sensor-to-controller (S-C) channels can be adapted, the self-triggered control architecture can be considerably simplified, leveraging a delicate observer-based deadbeat controller to eliminate the need for running the controller in parallel at the encoder side. To account for denial-of-service (DoS) in the S-C channel, the proposed output encoding and self-triggered control schemes are further made resilient. It is shown that a linear time-invariant system can be exponentially stabilized if some conditions on the average DoS duration time are met. There is a trade-off between the maximum inter-sampling time and the resilience against DoS attacks. Finally, a numerical example is presented to demonstrate the practical merits of the proposed self-triggered control schemes and associated theory. © 2023, CC BY.

关键词： Controllers