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[1] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[2] Patrick D. McDaniel,et al. Extending Defensive Distillation , 2017, ArXiv.
[3] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[4] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[5] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[6] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Medhat A. Moussa,et al. Attacking Binarized Neural Networks , 2017, ICLR.
[8] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[9] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[10] Ian J. Goodfellow,et al. Technical Report on the CleverHans v2.1.0 Adversarial Examples Library , 2016 .
[11] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[12] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[13] Mingyan Liu,et al. Spatially Transformed Adversarial Examples , 2018, ICLR.
[14] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[15] Patrick D. McDaniel,et al. On the (Statistical) Detection of Adversarial Examples , 2017, ArXiv.
[16] Jinfeng Yi,et al. EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples , 2017, AAAI.
[17] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[18] Suman Jana,et al. DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[19] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[20] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[21] R. Venkatesh Babu,et al. Fast Feature Fool: A data independent approach to universal adversarial perturbations , 2017, BMVC.
[22] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[23] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[24] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[25] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[26] Patrick D. McDaniel,et al. Cleverhans V0.1: an Adversarial Machine Learning Library , 2016, ArXiv.
[27] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[28] Kouichi Sakurai,et al. Attacking convolutional neural network using differential evolution , 2018, IPSJ Transactions on Computer Vision and Applications.
[29] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[30] Matthias Bethge,et al. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.
[31] Patrick D. McDaniel,et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.
[32] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[33] Mykel J. Kochenderfer,et al. Towards Proving the Adversarial Robustness of Deep Neural Networks , 2017, FVAV@iFM.
[34] Uri Shaham,et al. Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization , 2015, ArXiv.
[35] Min Wu,et al. Safety Verification of Deep Neural Networks , 2016, CAV.
[36] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[37] Matthew Wicker,et al. Feature-Guided Black-Box Safety Testing of Deep Neural Networks , 2017, TACAS.