publication . Preprint . 2017

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

Lee, Hyeungill; Han, Sungyeob; Lee, Jungwoo;
Open Access English
  • Published: 09 May 2017
We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularizat...
arXiv: Computer Science::Cryptography and Security
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Computer Science - Learning, Statistics - Machine Learning
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19 references, page 1 of 2

[1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2015.

[2] D. Berthelot, T. Schumm, and L. Metz. BEGAN: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717, 2017. [OpenAIRE]

[3] X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Aistats, volume 9, pages 249-256, 2010.

[4] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014.

[5] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016. http: //

[6] I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.

[7] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6): 82-97, 2012.

[8] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.

[9] D. Kingma and J. Ba. arXiv:1412.6980, 2014.

[10] A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009.

[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.

[12] T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii. Distributional smoothing with virtual adversarial training. arXiv preprint arXiv:1507.00677, 2015. [OpenAIRE]

[13] N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami. Distillation as a defense to adversarial perturbations against deep neural networks. In Security and Privacy (SP), 2016 IEEE Symposium on, pages 582-597. IEEE, 2016.

[14] A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.

[15] U. Shaham, Y. Yamada, and S. Negahban. Understanding adversarial training: Increasing local stability of neural nets through robust optimization. arXiv preprint arXiv:1511.05432, 2015. [OpenAIRE]

19 references, page 1 of 2
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