A survey of practical adversarial example attacks
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[1] Xiaogang Wang,et al. Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Andrea Vedaldi,et al. Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Yang Song,et al. PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.
[4] Xiaofeng Wang,et al. Invisible Mask: Practical Attacks on Face Recognition with Infrared , 2018, ArXiv.
[5] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[6] Colin Raffel,et al. Thermometer Encoding: One Hot Way To Resist Adversarial Examples , 2018, ICLR.
[7] Kouichi Sakurai,et al. One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.
[8] Dawn Song,et al. Robust Physical-World Attacks on Deep Learning Models , 2017, 1707.08945.
[9] Hao Chen,et al. MagNet: A Two-Pronged Defense against Adversarial Examples , 2017, CCS.
[10] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[11] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[12] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[13] Kamyar Azizzadenesheli,et al. Stochastic Activation Pruning for Robust Adversarial Defense , 2018, ICLR.
[14] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[15] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[16] 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).
[17] Dawn Xiaodong Song,et al. Exploring the Space of Black-box Attacks on Deep Neural Networks , 2017, ArXiv.
[18] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[19] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[20] Ananthram Swami,et al. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples , 2016, ArXiv.
[21] David Wagner,et al. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.
[22] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[23] Pan He,et al. Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[24] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[25] Zhitao Gong,et al. Adversarial and Clean Data Are Not Twins , 2017, aiDM@SIGMOD.
[26] Rama Chellappa,et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.
[27] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).