1. Ma, X., Niu, Yu., Gu, L., Wang, Yi., Zhao, Yi., Bailey, J., and Lu, F., Understanding adversarial attacks on deep learning based medical image analysis systems, Pattern Recognit., 2020, vol. 110, p. 107332. https://doi.org/10.1016/j.patcog.2020.107332
2. Hospital viruses: Fake cancerous nodes in CT scans, created by malware, trick radiologists, The Washington Post, 2019. https://www.washingtonpost.com/technology/2019/04/03/hospital-viruses-fake-cancerous-nodes-ct-scans-created-by-malware-trick-radiologists/. Cited February 15, 2021.
3. Pitropakis, N., Panaousis, E., Giannetsos, T., Anastasiadis, E., and Loukas, G., A taxonomy and survey of attacks against machine learning, Comput. Sci. Rev., 2019, vol. 34, p. 100199. https://doi.org/10.1016/j.cosrev.2019.100199
4. Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., and Mukhopadhyay, D., Adversarial attacks and defences: a survey, 2018. arXiv:1810.00069 [cs.LG]
5. Barreno, M., Nelson, B., Sears, R., Joseph, A.D., and Tygar, J.D., Can machine learning be secure?, ASIACC-S ’06: Proc. 2006 ACM Symp. on Information, Computer and Communication Security, Taipei, Taiwan, 2006, New York: Association for Computing Machinery, 2006, pp. 16–25. https://doi.org/10.1145/1128817.1128824