Defense Against Adversarial Attacks with Saak Transform

6 August 2018

Papers citing "Defense Against Adversarial Attacks with Saak Transform"

28 / 28 papers shown

Title
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness Mohammadamin Tavakoli Forest Agostinelli Pierre Baldi AAML FAtt 115 39 0 16 Jun 2020
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples Zihao Liu Qi Liu Tao Liu Nuo Xu Xue Lin Yanzhi Wang Wujie Wen AAML MQ 33 260 0 14 Mar 2018
Spatially Transformed Adversarial Examples Chaowei Xiao Jun-Yan Zhu Yue Liu Warren He M. Liu D. Song AAML 62 520 0 08 Jan 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 145 679 0 26 Nov 2017
Defense against Universal Adversarial Perturbations Naveed Akhtar Jian Liu Ajmal Mian AAML 60 207 0 16 Nov 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 86 1,399 0 31 Oct 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 99 2,315 0 24 Oct 2017
On Data-Driven Saak Transform C.-C. Jay Kuo Yueru Chen AI4TS 44 94 0 11 Oct 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 167 2,712 0 19 May 2017
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Li-Wei Chen Michael E. Kounavis Duen Horng Chau AAML 45 304 0 08 May 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 1.1K 20,747 0 17 Apr 2017
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics Xin Li Fuxin Li GAN AAML 87 365 0 22 Dec 2016
End-to-end Learning of Driving Models from Large-scale Video Datasets Huazhe Xu Yang Gao Feng Yu Trevor Darrell 81 824 0 04 Dec 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 126 1,727 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 450 3,124 0 04 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 113 2,520 0 26 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 631 36,599 0 25 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 170 8,513 0 16 Aug 2016
A study of the effect of JPG compression on adversarial images Gintare Karolina Dziugaite Zoubin Ghahramani Daniel M. Roy AAML 73 532 0 02 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 494 5,878 0 08 Jul 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 66 3,947 0 24 Nov 2015
A Unified Gradient Regularization Family for Adversarial Examples Chunchuan Lyu Kaizhu Huang Hai-Ning Liang AAML 42 207 0 19 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 98 4,878 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 48 3,061 0 14 Nov 2015
FaceNet: A Unified Embedding for Face Recognition and Clustering Florian Schroff Dmitry Kalenichenko James Philbin 3DH 281 13,079 0 12 Mar 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 179 18,922 0 20 Dec 2014
Towards Deep Neural Network Architectures Robust to Adversarial Examples S. Gu Luca Rigazio AAML 68 839 0 11 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 185 14,831 1 21 Dec 2013