DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning

31 July 2022

Papers citing "DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning"

5 / 5 papers shown

Title
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging Chao-Tsung Huang 32 10 0 19 Apr 2021
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 50 63 0 02 Mar 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 50 101 0 16 Oct 2019
On the Limitation of MagNet Defense against $L_1$ -based Adversarial Examples Pei-Hsuan Lu Pin-Yu Chen Kang-Cheng Chen Chia-Mu Yu AAML 41 19 0 14 Apr 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,833 0 08 Jul 2016