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While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.
On the surprising behavior of distance metrics in high dimensional space
Aggarwal, C. C., Hinneburg, A., and Keim, D. A. (2001) · 2001
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Estimating the number of clusters in a data set via the gap statistic
Tibshirani, R., Walther, G., and Hastie, T. (2001) · 2001
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Misleading learners: Co-opting your spam filter
Nelson, B., Barreno, M., Chi, F. J., Joseph, A. D., Rubinstein, B. I., Saini, U., Sutton, C., Tygar, J., and Xia, K. (2009) · 2009
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The security of machine learning
Barreno, M., Nelson, B., Joseph, A. D., and Tygar, J. (2010) · 2010
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Online anomaly detection under adversarial impact
Kloft, M. and Laskov, P. (2010) · 2010
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Behavior of Machine Learning Algorithms in Adversarial Environments
Nelson, B. A. (2010) · 2010
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Adversarial machine learning
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. D. (2011) · 2011
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A few useful things to know about machine learning
Domingos, P. (2012) · 2012
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Security analysis of online centroid anomaly detection
Kloft, M. and Laskov, P. (2012) · 2012
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Convolutional neural networks for sentence classification
Kim, Y. (2014) · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Implementing a cnn for text classification in tensorflow
Britz, D. (2015) · 2015
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Towards the science of security and privacy in machine learning
Papernot, N., McDaniel, P., Sinha, A., and Wellman, M. (2016) · 2016
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S. (2017) · 2017
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Neural trojans
Liu, Y., Xie, Y., and Srivastava, A. (2017b) · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F. (2017) · 2017
Later among the works it cites.
Certified defenses for data poisoning attacks
Steinhardt, J., Koh, P. W. W., and Liang, P. S. (2017) · 2017
Later among the works it cites.
Generative poisoning attack method against neural networks
Yang, C., Wu, Q., Li, H., and Chen, Y. (2017) · 2017
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Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (2016) · 2016
Cited alongside, same era.
Mitigating poisoning attacks on machine learning models: A data provenance based approach
Baracaldo, N., Chen, B., Ludwig, H., and Safavi, J. A. (2017) · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. (2017) · 2017
Cited alongside, same era.
Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X. (2017a)
Cited in the paper.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Liu, K., Dolan-Gavitt, B., and Garg, S. (2018) · 2018
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Adversarial robustness toolbox v0. 2.2
Nicolae, M.-I., Sinn, M., Tran, M. N., Rawat, A., Wistuba, M., Zantedeschi, V., Molloy, I. M., and Edwards, B. (2018) · 2018
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