318 research outputs found
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
Head Pose Estimation via Manifold Learning
For the last decades, manifold learning has shown its advantage of efficient non-linear dimensionality reduction in data analysis. Based on the assumption that informative and discriminative representation of the data lies on a low-dimensional smooth manifold which implicitly embedded in the original high-dimensional space, manifold learning aims to learn the low-dimensional representation following some geometrical protocols, such as preserving piecewise local structure of the original data. Manifold learning also plays an important role in the applications of computer vision, i.e., face image analysis. According to the observations that many face-related research is benefitted by the head pose estimation, and the continuous variation of head pose can be modelled and interpreted as a low-dimensional smooth manifold, we will focus on the head pose estimation via manifold learning in this chapter. Generally, head pose is hard to directly explore from the high-dimensional space interpreted as face images, which is, however, can be efficiently represented in low-dimensional manifold. Therefore, in this chapter, classical manifold learning algorithms are introduced and the corresponding application on head pose estimation are elaborated. Several extensions of manifold learning algorithms which are developed especially for head pose estimation are also discussed and compared
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