Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks
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Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
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These authors contributed equally to this work.
Big Data Cogn. Comput. 2020, 4(2), 11; https://doi.org/10.3390/bdcc4020011
Received: 29 April 2020 / Revised: 16 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using combination of pre-trained convolutional neural network and an external GAN, that is, Pix2Pix conditional GAN, to determine the transformations between adversarial examples and clean data, and to automatically synthesize new adversarial examples. These adversarial examples are employed to strengthen the model, attack, and defense in an iterative pipeline. Our simulation results demonstrate the success of the proposed method.
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Keywords:
adversarial machine learning; botnet detection; generative adversarial networks; machine learning
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MDPI and ACS Style
Taheri, S.; Khormali, A.; Salem, M.; Yuan, J.-S. Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks. Big Data Cogn. Comput. 2020, 4, 11. https://doi.org/10.3390/bdcc4020011
AMA Style
Taheri S, Khormali A, Salem M, Yuan J-S. Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks. Big Data and Cognitive Computing. 2020; 4(2):11. https://doi.org/10.3390/bdcc4020011
Chicago/Turabian StyleTaheri, Shayan; Khormali, Aminollah; Salem, Milad; Yuan, Jiann-Shiun. 2020. "Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks" Big Data Cogn. Comput. 4, no. 2: 11. https://doi.org/10.3390/bdcc4020011
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