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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 4/2021

20.01.2020

An Evolutionary-Based Black-Box Attack to Deep Neural Network Classifiers

verfasst von: Yutian Zhou, Yu-an Tan, Quanxin Zhang, Xiaohui Kuang, Yahong Han, Jingjing Hu

Erschienen in: Mobile Networks and Applications | Ausgabe 4/2021

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Abstract

Deep neural networks are susceptible to tiny crafted adversarial perturbations which are always added to all the pixels of the image to craft an adversarial example. Most of the existing adversarial attacks can reduce the L2 distance between the adversarial image and the source image to a minimum but ignore the L0 distance which is still huge. To address this issue, we introduce a new black-box adversarial attack based on evolutionary method and bisection method, which can greatly reduce the L0 distance while limiting the L2 distance. By flipping pixels of the target image, an adversarial example is generated, in which a small number of pixels come from the target image and the rest pixels are from the source image. Experiments show that our attack method is able to generate high quality adversarial examples steadily. Especially for generating adversarial examples for large scale images, our method performs better.
Vorheriger Artikel Cryptanalysis of a Public Key Cryptosystem Based on Data Complexity under Quantum Environment
Nächster Artikel Network Risk Assessment Based on Baum Welch Algorithm and HMM

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Metadaten
Titel
An Evolutionary-Based Black-Box Attack to Deep Neural Network Classifiers
verfasst von
Yutian Zhou
Yu-an Tan
Quanxin Zhang
Xiaohui Kuang
Yahong Han
Jingjing Hu
Publikationsdatum
20.01.2020
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 4/2021
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-019-01499-x

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