Neural Trojans

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1988

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1998

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S. Hawkins, H. He, G. Williams, and R. Baxter, “Outlier detection using replicator neural networks,” in International Conference on Data Warehousing and Knowledge Discovery . Springer, 2002, pp. 170–180

2002

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2009

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M. Barreno, B. Nelson, A. D. Joseph, and J. Tygar, “The security of machine learning,” Machine Learning , vol. 81, no. 2, pp. 121–148, 2010

2010

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M. Tehranipoor and F. Koushanfar, “A survey of hardware trojan taxonomy and detection,” IEEE Design & Test of Computers , vol. 27, no. 1, 2010

Original

2010

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L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM, 2011, pp. 43–58

2011

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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105

2012

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K. P. Murphy, Machine learning: a probabilistic perspective . MIT press, 2012

2012

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B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” arXiv preprint arXiv:1206.6389 , 2012

Original

2012

Cited alongside, same era.

T. N. Sainath, A.-r. Mohamed, B. Kingsbury, and B. Ramabhadran, “Deep convolutional neural networks for lvcsr,” in Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference on . IEEE, 2013, pp. 8614–8618

2013

Cited alongside, same era.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv preprint arXiv:1312.6229 , 2013

Original

2013

Cited alongside, same era.

G. E. Dahl, J. W. Stokes, L. Deng, and D. Yu, “Large-scale malware classification using random projections and neural networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on . IEEE, 2013, pp. 3422–3426

2013

Cited alongside, same era.

S. Mei and X. Zhu, “Using machine teaching to identify optimal training-set attacks on machine learners.” in AAAI , 2015, pp. 2871–2877

2015

Later among the works it cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778

2016

Later among the works it cites.

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 (EuroS&P), 2016 IEEE European Symposium on . IEEE, 2016, pp. 372–387

2016

Later among the works it cites.

C. Yang, B. Liu, H. Li, Y. Chen, W. Wen, M. Barnell, Q. Wu, and J. Rajendran, “Security of neuromorphic computing: thwarting learning attacks using memristor’s obsolescence effect,” in Proceedings of the 35th International Conference on Computer-Aided Design . ACM, 2016, p. 97

2016

Later among the works it cites.