Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples

28 July 2019

Papers citing "Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples"

7 / 7 papers shown

Title
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 30 2 0 31 Jul 2022
Exact Feature Collisions in Neural Networks Utku Ozbulak Manvel Gasparyan Shodhan Rao W. D. Neve Arnout Van Messem AAML 34 1 0 31 May 2022
Towards Feature Space Adversarial Attack Qiuling Xu Guanhong Tao Shuyang Cheng Xinming Zhang GAN AAML 25 25 0 26 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 109 824 0 19 Feb 2020
Deflecting Adversarial Attacks Yao Qin Nicholas Frosst Colin Raffel G. Cottrell Geoffrey E. Hinton AAML 30 15 0 18 Feb 2020
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Yao Qin Nicholas Frosst S. Sabour Colin Raffel G. Cottrell Geoffrey E. Hinton GAN AAML 27 71 0 05 Jul 2019
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 314 3,115 0 04 Nov 2016