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Laskov, \"Poisoning attacks against support vector machines,\" in Proceedings of the 29th International Coference on International Conference on Machine Learning, pp. 1467--1474, Omnipress, 2012."},{"key":"e_1_3_2_1_4_1","volume-title":"Badnets: Identifying vulnerabilities in the machine learning model supply chain,\" arXiv preprint arXiv:1708.06733","author":"Gu T.","year":"2017","unstructured":"T. Gu , B. Dolan-Gavitt , and S. Garg , \" Badnets: Identifying vulnerabilities in the machine learning model supply chain,\" arXiv preprint arXiv:1708.06733 , 2017 . T. Gu, B. Dolan-Gavitt, and S. Garg, \"Badnets: Identifying vulnerabilities in the machine learning model supply chain,\" arXiv preprint arXiv:1708.06733, 2017."},{"key":"e_1_3_2_1_5_1","volume-title":"Mnist handwritten digit database,\" AT&T Labs [Online]. Available: http:\/\/yann. lecun. com\/exdb\/mnist","author":"LeCun Y.","year":"2010","unstructured":"Y. LeCun , C. Cortes , and C. J. Burges , \" Mnist handwritten digit database,\" AT&T Labs [Online]. Available: http:\/\/yann. lecun. com\/exdb\/mnist , vol. 2 , 2010 . Y. LeCun, C. Cortes, and C. J. Burges, \"Mnist handwritten digit database,\" AT&T Labs [Online]. Available: http:\/\/yann. lecun. com\/exdb\/mnist, vol. 2, 2010."},{"key":"e_1_3_2_1_6_1","volume-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,\" arXiv preprint arXiv:1708.07747","author":"Xiao H.","year":"2017","unstructured":"H. Xiao , K. Rasul , and R. Vollgraf , \" Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,\" arXiv preprint arXiv:1708.07747 , 2017 . H. Xiao, K. Rasul, and R. Vollgraf, \"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,\" arXiv preprint arXiv:1708.07747, 2017."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"key":"e_1_3_2_1_8_1","volume-title":"Explaining and harnessing adversarial examples,\" in International Conference on Learning Representations","author":"Goodfellow I.","year":"2015","unstructured":"I. Goodfellow , J. Shlens , and C. Szegedy , \" Explaining and harnessing adversarial examples,\" in International Conference on Learning Representations , 2015 . I. Goodfellow, J. Shlens, and C. Szegedy, \"Explaining and harnessing adversarial examples,\" in International Conference on Learning Representations, 2015."},{"key":"e_1_3_2_1_9_1","volume-title":"Adversarial examples in the physical world,\" ICLR Workshop","author":"Kurakin A.","year":"2017","unstructured":"A. Kurakin , I. Goodfellow , and S. Bengio , \" Adversarial examples in the physical world,\" ICLR Workshop , 2017 . A. Kurakin, I. Goodfellow, and S. Bengio, \"Adversarial examples in the physical world,\" ICLR Workshop, 2017."},{"key":"e_1_3_2_1_10_1","volume-title":"Deepfool: a simple and accurate method to fool deep neural networks,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Moosavi-Dezfooli S.-M.","year":"2016","unstructured":"S.-M. Moosavi-Dezfooli , A. Fawzi , and P. Frossard , \" Deepfool: a simple and accurate method to fool deep neural networks,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016 . S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, \"Deepfool: a simple and accurate method to fool deep neural networks,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016."},{"key":"e_1_3_2_1_11_1","first-page":"372","volume-title":"2016 IEEE European Symposium on","author":"Papernot N.","year":"2016","unstructured":"N. Papernot , P. McDaniel , S. Jha , M. Fredrikson , Z. B. Celik , and A. Swami , \" The limitations of deep learning in adversarial settings,\" in Security and Privacy (Euro S&P) , 2016 IEEE European Symposium on , pp. 372 -- 387 , 2016 . N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, \"The limitations of deep learning in adversarial settings,\" in Security and Privacy (Euro S&P), 2016 IEEE European Symposium on, pp. 372--387, 2016."},{"key":"e_1_3_2_1_12_1","first-page":"39","volume-title":"2017 IEEE Symposium on","author":"Carlini N.","year":"2017","unstructured":"N. Carlini and D. Wagner , \"Towards evaluating the robustness of neural networks,\" in Security and Privacy (SP) , 2017 IEEE Symposium on , pp. 39 -- 57 , IEEE, 2017 . N. Carlini and D.Wagner, \"Towards evaluating the robustness of neural networks,\" in Security and Privacy (SP), 2017 IEEE Symposium on, pp. 39--57, IEEE, 2017."},{"key":"e_1_3_2_1_13_1","volume-title":"Generative poisoning attack method against neural networks,\" arXiv preprint arXiv:1703.01340","author":"Yang C.","year":"2017","unstructured":"C. Yang , Q. Wu , H. Li , and Y. Chen , \" Generative poisoning attack method against neural networks,\" arXiv preprint arXiv:1703.01340 , 2017 .M. Mozaffari-Kermani, S. Sur-Kolay, A. Raghunathan, and N. K. Jha, \"Systematic poisoning attacks on and defenses for machine learning in healthcare,\" IEEE journal of biomedical and health informatics, vol. 19 , no. 6, pp. 1893--1905, 2015. C. Yang, Q. Wu, H. Li, and Y. Chen, \"Generative poisoning attack method against neural networks,\" arXiv preprint arXiv:1703.01340, 2017.M. Mozaffari-Kermani, S. Sur-Kolay, A. Raghunathan, and N. K. Jha, \"Systematic poisoning attacks on and defenses for machine learning in healthcare,\" IEEE journal of biomedical and health informatics, vol. 19, no. 6, pp. 1893--1905, 2015."},{"key":"e_1_3_2_1_14_1","volume-title":"Trojaning attack on neural networks,\" NDSS","author":"Liu Y.","year":"2018","unstructured":"Y. Liu , S. Ma , Y. Aafer , W.-C. Lee , J. Zhai , W. Wang , and X. Zhang , \" Trojaning attack on neural networks,\" NDSS , 2018 . Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, \"Trojaning attack on neural networks,\" NDSS, 2018."},{"key":"e_1_3_2_1_15_1","volume-title":"Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,\" 2019 IEEE Symposium on Security and Privacy (SP)","author":"Wang B.","year":"2019","unstructured":"B. Wang , Y. Yao , S. Shan , H. Li , B. Viswanath , H. Zheng , and B. Y. Zhao , \" Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,\" 2019 IEEE Symposium on Security and Privacy (SP) 2019 . B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, \"Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,\" 2019 IEEE Symposium on Security and Privacy (SP) 2019."},{"key":"e_1_3_2_1_16_1","volume-title":"Hardware trojan attacks on neural networks,\" arXiv preprint arXiv:1806.05768","author":"Clements J.","year":"2018","unstructured":"J. Clements and Y. Lao , \" Hardware trojan attacks on neural networks,\" arXiv preprint arXiv:1806.05768 , 2018 . J. Clements and Y. Lao, \"Hardware trojan attacks on neural networks,\" arXiv preprint arXiv:1806.05768, 2018."},{"key":"e_1_3_2_1_17_1","volume-title":"Gradient-based learning applied to document recognition,\" Proceedings of the IEEE","author":"LeCun Y.","unstructured":"Y. LeCun , L. Bottou , Y. Bengio , and P. 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