Abstract
Machine learning is becoming more and more utilized as a tool for businesses and governments to aid in decision making and automation processes. These systems are also susceptible to attacks by an adversary, who may try evading or corrupting the system. In this paper, we survey the current landscape of research in this field, and provide analysis of the overall results and of the trends in research. We also identify several topics which can better define the categorization.
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References
Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I.P., Tygar, J.D.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security Artificial Intelligence, pp. 43–58 (2011)
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: Proceedings - 2016 IEEE Symposium on Security and Privacy, SP 2016, pp. 582–597 (2016)
Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. CoRR abs/1605.07277 (2016)
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. CoRR abs/1602.02697(2016)
Biggio, B., Fumera, G., Roli, F.: Adversarial pattern classification using multiple classifiers and randomisation. In: da Vitoria Lobo, N., et al. (eds.) SSPR /SPR 2008. LNCS, vol. 5342, pp. 500–509. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89689-0_54
Biggio, B., Corona, I., Fumera, G., Giacinto, G., Roli, F.: Bagging classifiers for fighting poisoning attacks in adversarial classification tasks. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 350–359. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21557-5_37
Villacorta, P.J., Pelta, D.A.: Exploiting adversarial uncertainty in robotic patrolling: a simulation-based analysis. In: Greco, S., et al. (eds.) IPMU 2012 Part IV. CCIS, vol. 300, pp. 529–538. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31724-8_55
Papernot, N., Mcdaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: Proceedings - 2016 IEEE European Symposium Security Privacy, EURO S P 2016, pp. 372–387 (2016)
Kumar, A., Mehta, S.: A survey on resilient machine learning. CoRR, abs/1707.03184 (2017)
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Thomas, S., Tabrizi, N. (2018). Adversarial Machine Learning: A Literature Review. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_26
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DOI: https://doi.org/10.1007/978-3-319-96136-1_26
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