Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks

20 July 2021

Papers citing "Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks"

10 / 10 papers shown

Title
Defensive Approximation: Securing CNNs using Approximate Computing Amira Guesmi Ihsen Alouani Khaled N. Khasawneh M. Baklouti T. Frikha Mohamed Abid Nael B. Abu-Ghazaleh AAML 31 38 0 13 Jun 2020
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 95 103 0 16 Oct 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 116 2,018 0 08 Feb 2019
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples Pin-Yu Chen Yash Sharma Huan Zhang Jinfeng Yi Cho-Jui Hsieh AAML 59 639 0 13 Sep 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 68 710 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 56 947 0 14 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 458 3,124 0 04 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 117 2,520 0 26 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 183 8,513 0 16 Aug 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 204 14,831 1 21 Dec 2013