EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

21 April 2020

Papers citing "EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks"

20 / 20 papers shown

Title
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Amira Guesmi B. Ouni Mohamed Bennai AAML 128 0 0 22 Nov 2024
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks Priyadarshini Panda I. Chakraborty Kaushik Roy AAML 46 40 0 08 Feb 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 60 437 0 25 Jan 2019
Training Deep Neural Networks with 8-bit Floating Point Numbers Naigang Wang Jungwook Choi D. Brand Chia-Yu Chen K. Gopalakrishnan MQ 56 500 0 19 Dec 2018
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions Adnan Siraj Rakin Jinfeng Yi Boqing Gong Deliang Fan AAML MQ 43 50 0 18 Jul 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 147 680 0 26 Nov 2017
Attacking Binarized Neural Networks A. Galloway Graham W. Taylor M. Moussa MQ AAML 60 105 0 01 Nov 2017
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Thilo Strauss Markus Hanselmann Andrej Junginger Holger Ulmer AAML 55 135 0 11 Sep 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 265 12,029 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 71 241 0 15 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,720 0 19 May 2017
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library Nicolas Papernot Fartash Faghri Nicholas Carlini Ian Goodfellow Reuben Feinman ... David Berthelot P. Hendricks Jonas Rauber Rujun Long Patrick McDaniel AAML 56 512 0 03 Oct 2016
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations Itay Hubara Matthieu Courbariaux Daniel Soudry Ran El-Yaniv Yoshua Bengio MQ 137 1,861 0 22 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 222 8,533 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,885 0 08 Jul 2016
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients Shuchang Zhou Yuxin Wu Zekun Ni Xinyu Zhou He Wen Yuheng Zou MQ 112 2,085 0 20 Jun 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 76 3,955 0 24 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 60 3,066 0 14 Nov 2015
BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux Yoshua Bengio J. David MQ 181 2,984 0 02 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 229 19,017 0 20 Dec 2014