Adversarial Examples: Attacks and Defenses for Deep Learning

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 9; pp. 2805 - 2824
• Main Authors: Yuan, Xiaoyong, He, Pan, Zhu, Qile, Li, Xiaolin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial examples; Artificial neural networks; Computational modeling; Computer architecture; Data models; Deep learning; deep learning (DL); deep neural network (DNN); Feature extraction; Machine learning; Neural networks; Safety critical; Security; Task analysis; Taxonomy
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2886017

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.