One-Pixel Adversarial Example that is Safe for Friendly Deep Neural Networks

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Deep neural networks (DNNs) offer superior performance in machine learning tasks such as image recognition, speech recognition, pattern analysis, and intrusion detection. In this paper, we propose a one-pixel adversarial example that is safe for friendly deep neural networks. By modifying only one pixel, our proposed method generates a one-pixel-safe adversarial example that can be misclassified by an enemy classifier and correctly classified by a friendly classifier. To verify the performance of the proposed method, we used the CIFAR-10 dataset, ResNet model classifiers, and the Tensorflow library in our experiments. Results show that the proposed method modified only one pixel to achieve success rates of 13.5% and 26.0% in targeted and untargeted attacks, respectively. The success rate is slightly lower than that of the conventional one-pixel method, which has success rates of 15% and 33.5% in targeted and untargeted attacks, respectively; however, this method protects 100\% of the friendly classifiers. In addition, if the proposed method modifies five pixels, this method can achieve success rates of 20.5% and 52.0% in targeted and untargeted attacks, respectively.
Publisher
Korea Institute of Information Security & Cryptology
Issue Date
2018-08-23
Language
English
Citation

The 19th World Conference on Information Security Applications (WISA 2018)

URI
http://hdl.handle.net/10203/244922
Appears in Collection
CS-Conference Papers(학술회의논문)
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