Adversarial Examples: Attacks and Defenses for Deep Learning.

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2019
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Abstract
With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples. Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.
Reference Key
yuan2019adversarialieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuan, Xiaoyong;He, Pan;Zhu, Qile;Li, Xiaolin;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2018.2886017
URL
Keywords
epicardial fat fat segmentation fruitfly algorithm gwo modified region growing optimal neural network

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