Defending Against Adversarial Samples Without Security through Obscurity
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Wenbo Guo | Xue Liu | Lin Lin | C. Lee Giles | Xinyu Xing | Sui Huang | Alexander Ororbia | Qinglong Wang | Kaixuan Zhang | Alexander Ororbia | Xue Liu | Qinglong Wang | Kaixuan Zhang | Xinyu Xing | Wenbo Guo | Sui Huang | Lin Lin
[1] Somesh Jha,et al. Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training , 2017, ICML.
[2] Mingyan Liu,et al. Generating Adversarial Examples with Adversarial Networks , 2018, IJCAI.
[3] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[4] Kevin W. Hamlen,et al. Frankenstein: Stitching Malware from Benign Binaries , 2012, WOOT.
[5] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[6] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[7] John C. Duchi,et al. Certifiable Distributional Robustness with Principled Adversarial Training , 2017, ArXiv.
[8] Luca Maria Gambardella,et al. Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition , 2010, ArXiv.
[9] Daniel Kifer,et al. Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization , 2016, ArXiv.
[10] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[11] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[12] Sameer Singh,et al. Generating Natural Adversarial Examples , 2017, ICLR.
[13] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[14] John W. Sammon,et al. A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.
[15] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[16] Wenbo Guo,et al. Adversary Resistant Deep Neural Networks with an Application to Malware Detection , 2016, KDD.
[17] Hao Chen,et al. MagNet: A Two-Pronged Defense against Adversarial Examples , 2017, CCS.
[18] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[19] Antonio Criminisi,et al. Measuring Neural Net Robustness with Constraints , 2016, NIPS.
[20] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[21] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[22] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[23] Eric O. Postma,et al. Dimensionality Reduction: A Comparative Review , 2008 .
[24] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[25] Prateek Mittal,et al. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers , 2017, ArXiv.
[26] Xiaoyu Cao,et al. Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification , 2017, ACSAC.
[27] David J. Fleet,et al. Adversarial Manipulation of Deep Representations , 2015, ICLR.
[28] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[29] Junfeng Yang,et al. DeepXplore , 2019, Commun. ACM.
[30] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[31] David Slater,et al. Malicious Behavior Detection using Windows Audit Logs , 2015, AISec@CCS.
[32] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[33] Trevor Darrell,et al. Fooling Vision and Language Models Despite Localization and Attention Mechanism , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[34] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).