Security Risks in Deep Learning Implementations
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Kang Li | Qixue Xiao | Deyue Zhang | Weilin Xu | Weilin Xu | Qixue Xiao | Kang Li | Deyue Zhang
[1] Michael P. Wellman,et al. Towards the Science of Security and Privacy in Machine Learning , 2016, ArXiv.
[2] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[3] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[4] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[5] Paul Barford,et al. Data Poisoning Attacks against Autoregressive Models , 2016, AAAI.
[6] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[7] Tudor Dumitras,et al. Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning , 2017, ArXiv.
[8] Blaine Nelson,et al. Poisoning Attacks against Support Vector Machines , 2012, ICML.
[9] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[10] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[11] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[13] Blaine Nelson,et al. The security of machine learning , 2010, Machine Learning.