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Deep neural network (DNN) as a popular machine learning model is found to be vulnerable to adversarial attack.
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
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P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “Zoo: Zeroth Order Optimization Based Black-Box Attacks to Deep Neural Networks without Training Substitute Models,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , 2017, pp. 15–26
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N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical Black-Box Attacks against Machine Learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security , 2017, pp. 506–519
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J. Buckman, A. Roy, C. Raffel, and I. Goodfellow, “Thermometer Encoding: One Hot Way to Resist Adversarial Examples,” in Proceedings of the International Conference on Learning Representations , 2018
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