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Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs.
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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 , ser. ASIA CCS ’17. New York, NY, USA: ACM, 2017, pp. 506–519. [Online]. Available: http://doi.acm.org/10.1145/3052973.3053009
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N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE Symposium on Security and Privacy (SP) , 2017, pp. 39 – 57
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2015
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N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European Symposium on Security and Privacy (EuroS P) , 11 2016, pp. 372 – 387
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2017
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G. Carneiro, Y. Zheng, F. Xing, and L. Yang, Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis . Springer International Publishing, 2017, pp. 11 – 32
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E. Raff, J. Barker, J. Sylvester, R. Brandon, B. Catanzaro, and C. Nicholas, “Malware detection by eating a whole exe,” in The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence , 2018
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