Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
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Ling Shao | Roland Göcke | Jianbing Shen | Aamir Mustafa | Munawar Hayat | Salman H. Khan | Jianbing Shen | Roland Göcke | Munawar Hayat | Salman Hameed Khan | Aamir Mustafa | Ling Shao
[1] Raja Giryes,et al. Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization , 2018, ECCV.
[2] Chenchen Liu,et al. Interpreting Adversarial Robustness: A View from Decision Surface in Input Space , 2018, ArXiv.
[3] Harini Kannan,et al. Adversarial Logit Pairing , 2018, NIPS 2018.
[4] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[5] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[6] Jun Zhu,et al. Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Aditi Raghunathan,et al. Certified Defenses against Adversarial Examples , 2018, ICLR.
[8] Mark de Berg,et al. Computing the Maximum Overlap of Two Convex Polygons Under Translations , 1996, ISAAC.
[9] Dandelion Mané,et al. DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS , 2018 .
[10] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[11] Alan L. Yuille,et al. Improving Transferability of Adversarial Examples With Input Diversity , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Gabriela Csurka,et al. Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] John C. Duchi,et al. Certifiable Distributional Robustness with Principled Adversarial Training , 2017, ArXiv.
[14] Xiaolin Hu,et al. Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Dan Boneh,et al. The Space of Transferable Adversarial Examples , 2017, ArXiv.
[16] J. Zico Kolter,et al. Provable defenses against adversarial examples via the convex outer adversarial polytope , 2017, ICML.
[17] Andrew Slavin Ross,et al. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients , 2017, AAAI.
[18] Li Chen,et al. Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression , 2017, ArXiv.
[19] Yao Zhao,et al. Adversarial Attacks and Defences Competition , 2018, ArXiv.
[20] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[21] Ning Chen,et al. Improving Adversarial Robustness via Promoting Ensemble Diversity , 2019, ICML.
[22] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[23] Kun He,et al. Improving the Generalization of Adversarial Training with Domain Adaptation , 2018, ICLR.
[24] Conrad Sanderson,et al. Biometric Person Recognition: Face, Speech and Fusion , 2008 .
[25] Qi Zhao,et al. Foveation-based Mechanisms Alleviate Adversarial Examples , 2015, ArXiv.
[26] Ling Shao,et al. Image Super-Resolution as a Defense Against Adversarial Attacks , 2020, IEEE Transactions on Image Processing.
[27] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[28] Saibal Mukhopadhyay,et al. Cascade Adversarial Machine Learning Regularized with a Unified Embedding , 2017, ICLR.
[29] Yu Qiao,et al. A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.
[30] Mark de Berg,et al. Computing the Maximum Overlap of Two Convex Polygons under Translations , 1996, Theory of Computing Systems.
[31] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[32] 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).
[33] Kamyar Azizzadenesheli,et al. Stochastic Activation Pruning for Robust Adversarial Defense , 2018, ICLR.
[34] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[35] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[36] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[37] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[38] Taghi M. Khoshgoftaar,et al. Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.
[39] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Shin Ishii,et al. Distributional Smoothing with Virtual Adversarial Training , 2015, ICLR 2016.
[41] Ryan P. Adams,et al. Motivating the Rules of the Game for Adversarial Example Research , 2018, ArXiv.
[42] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[43] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[44] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.