TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers

Neural network (NN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks achieve state-of-the-art performance in feed-forward NNs and image recognition tasks, they do not perform as well on time series classification with recurrent neural network (RNN) models. This is because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity of time series data to perturbations challenges the traditional local optimization objective of the adversarial attack. In this paper, a black-box method called TSFool is proposed to efficiently craft highly-imperceptible adversarial time series for RNN classifiers. We propose a novel global optimization objective named Camouflage Coefficient to consider the imperceptibility of adversarial samples from the perspective of class distribution, and accordingly refine the adversarial attack as a multi-objective optimization problem to enhance the perturbation quality. To get rid of the dependence on gradient information, we also propose a new idea that introduces a representation model for RNN to capture deeply embedded vulnerable samples having otherness between their features and latent manifold, based on which the optimization solution can be heuristically approximated. Experiments on 10 UCR datasets are conducted to confirm that TSFool averagely outperforms existing methods with a 46.3% higher attack success rate, 87.4% smaller perturbation and 25.6% better Camouflage Coefficient at a similar time cost.Comment: 9 pages, 7 figure

Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI) techniques. Simulation results are given to illustrate the effectiveness of the proposed method

Generalized convexities and generalized gradients based on algebraic operations

AbstractIn this paper, we investigate properties of generalized convexities based on algebraic operations introduced by Ben Tal [A. Ben Tal, On generalized means and generalized convex functions, J. Optim. Theory Appl. 21 (1977) 1–13] and relations between these generalized convexities and generalized monotonicities. We also discuss the (h,φ)-generalized directional derivative and gradient, and explore the relation between this gradient and the Clarke generalized gradient. Definitions of some generalized averages of the values of a generalized convex function at n equally spaced points based on the algebraic operations are also presented and corresponding results are obtained. Finally, the (φ,γ)-convexity is defined and some properties of (φ,γ)-convex functions are derived