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Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 ( 2017 ). Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten. 2017. Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_11_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. Technical Report. Citeseer.  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. Technical Report. Citeseer."},{"key":"e_1_3_2_1_12_1","volume-title":"Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin , Ian Goodfellow , and Samy Bengio . 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 ( 2016 ). 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Leonid I Rudin, Stanley Osher, and Emad Fatemi. 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena 60, 1--4 (1992), 259--268."},{"key":"e_1_3_2_1_25_1","volume-title":"Learning representations by back-propagating errors. nature 323, 6088","author":"Rumelhart David E","year":"1986","unstructured":"David E Rumelhart , Geoffrey E Hinton , and Ronald J Williams . 1986. Learning representations by back-propagating errors. nature 323, 6088 ( 1986 ), 533--536. David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. nature 323, 6088 (1986), 533--536."},{"key":"e_1_3_2_1_26_1","volume-title":"A mathematical theory of communication. Bell system technical journal 27, 3","author":"Shannon Claude Elwood","year":"1948","unstructured":"Claude Elwood Shannon . 1948. A mathematical theory of communication. Bell system technical journal 27, 3 ( 1948 ), 379--423. Claude Elwood Shannon. 1948. 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