Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks

27 September 2019

Papers citing "Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks"

50 / 66 papers shown

Title
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Amira Guesmi B. Ouni Mohamed Bennai AAML 130 0 0 22 Nov 2024
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,839 0 06 May 2019
Defensive Quantization: When Efficiency Meets Robustness Ji Lin Chuang Gan Song Han MQ 69 203 0 17 Apr 2019
Defending against Whitebox Adversarial Attacks via Randomized Discretization Yuchen Zhang Percy Liang AAML 66 75 0 25 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 81 901 0 18 Feb 2019
Improving Adversarial Robustness of Ensembles with Diversity Training Sanjay Kariyappa Moinuddin K. Qureshi AAML FedML 55 135 0 28 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 129 2,549 0 24 Jan 2019
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks G. B. Hacene Vincent Gripon M. Arzel Nicolas Farrugia Yoshua Bengio MQ 26 14 0 29 Dec 2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models Sven Gowal Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel Chongli Qin J. Uesato Relja Arandjelović Timothy A. Mann Pushmeet Kohli AAML 76 556 0 30 Oct 2018
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only T. Zheng Changyou Chen K. Ren AAML 48 6 0 10 Oct 2018
Efficient Two-Step Adversarial Defense for Deep Neural Networks Ting-Jui Chang Yukun He Peng Li AAML 50 11 0 08 Oct 2018
Combinatorial Attacks on Binarized Neural Networks Elias Boutros Khalil Amrita Gupta B. Dilkina AAML 61 40 0 08 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 68 49 0 02 Oct 2018
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression Yiren Zhao Ilia Shumailov Robert D. Mullins Ross J. Anderson AAML 38 43 0 29 Sep 2018
A Survey on Methods and Theories of Quantized Neural Networks Yunhui Guo MQ 65 232 0 13 Aug 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
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 82 1,177 0 17 May 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 95 628 0 16 Mar 2018
Stochastic Activation Pruning for Robust Adversarial Defense Guneet Singh Dhillon Kamyar Azizzadenesheli Zachary Chase Lipton Jeremy Bernstein Jean Kossaifi Aran Khanna Anima Anandkumar AAML 73 547 0 05 Mar 2018
Understanding and Enhancing the Transferability of Adversarial Examples Lei Wu Zhanxing Zhu Cheng Tai E. Weinan AAML SILM 67 99 0 27 Feb 2018
Model compression via distillation and quantization A. Polino Razvan Pascanu Dan Alistarh MQ 83 731 0 15 Feb 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 147 604 0 15 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,185 0 01 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 105 968 0 29 Jan 2018
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 56 174 0 26 Dec 2017
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks Ruizhou Ding Z. Liu Rongye Shi Diana Marculescu R. D. Blanton MQ 41 37 0 02 Dec 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 113 1,058 0 06 Nov 2017
Provable defenses against adversarial examples via the convex outer adversarial polytope Eric Wong J. Zico Kolter AAML 123 1,501 0 02 Nov 2017
Attacking Binarized Neural Networks A. Galloway Graham W. Taylor M. Moussa MQ AAML 60 105 0 01 Nov 2017
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization Luis Muñoz-González Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C. Lupu Fabio Roli AAML 99 630 0 29 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 78 1,879 0 14 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 301 12,063 0 19 Jun 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 46 1,207 0 25 May 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 107 511 0 23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 118 1,857 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 77 1,266 0 04 Apr 2017
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly Jiajun Lu Theerasit Issaranon David A. Forsyth GAN 87 381 0 01 Apr 2017
Generative Poisoning Attack Method Against Neural Networks Chaofei Yang Qing Wu Hai Helen Li Yiran Chen AAML 59 218 0 03 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 90 893 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 76 713 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Towards the Limit of Network Quantization Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 44 194 0 05 Dec 2016
Trained Ternary Quantization Chenzhuo Zhu Song Han Huizi Mao W. Dally MQ 131 1,035 0 04 Dec 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 469 3,140 0 04 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 240 4,122 0 18 Oct 2016
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 65 512 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 140 1,863 0 22 Sep 2016
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples T. Tanay Lewis D. Griffin AAML 83 271 0 27 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 258 8,550 0 16 Aug 2016