暂无分享,去创建一个
Zhanxing Zhu | Ke Sun | Zhouchen Lin | Zhouchen Lin | Zhanxing Zhu | Ke Sun
[1] Brian McWilliams,et al. The Shattered Gradients Problem: If resnets are the answer, then what is the question? , 2017, ICML.
[2] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[3] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[4] Colin Raffel,et al. Thermometer Encoding: One Hot Way To Resist Adversarial Examples , 2018, ICLR.
[5] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[6] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[7] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[8] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[9] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[10] Adversarial Examples. THERMOMETER ENCODING: ONE HOT WAY TO RESIST , 2017 .
[11] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[12] Patrick D. McDaniel,et al. Making machine learning robust against adversarial inputs , 2018, Commun. ACM.
[13] Kibok Lee,et al. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples , 2017, ICLR.
[14] Rama Chellappa,et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.
[15] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[16] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[17] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[18] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[19] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[20] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[21] Saibal Mukhopadhyay,et al. Cascade Adversarial Machine Learning Regularized with a Unified Embedding , 2017, ICLR.
[22] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[23] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[24] Lei Wu,et al. Understanding and Enhancing the Transferability of Adversarial Examples , 2018, ArXiv.
[25] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).