Advanced Ensemble Adversarial Example on Unknown Deep Neural Network Classifiers

Hyun KWON  Yongchul KIM  Ki-Woong PARK  Hyunsoo YOON  Daeseon CHOI  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D    No.10    pp.2485-2500
Publication Date: 2018/10/01
Publicized: 2018/07/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7073
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
adversarial example,  neural networks,  ensemble adversarial example,  machine learning,  

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Deep neural networks (DNNs) are widely used in many applications such as image, voice, and pattern recognition. However, it has recently been shown that a DNN can be vulnerable to a small distortion in images that humans cannot distinguish. This type of attack is known as an adversarial example and is a significant threat to deep learning systems. The unknown-target-oriented generalized adversarial example that can deceive most DNN classifiers is even more threatening. We propose a generalized adversarial example attack method that can effectively attack unknown classifiers by using a hierarchical ensemble method. Our proposed scheme creates advanced ensemble adversarial examples to achieve reasonable attack success rates for unknown classifiers. Our experiment results show that the proposed method can achieve attack success rates for an unknown classifier of up to 9.25% and 18.94% higher on MNIST data and 4.1% and 13% higher on CIFAR10 data compared with the previous ensemble method and the conventional baseline method, respectively.