Bypassing Feature Squeezing by Increasing Adversary Strength

27 March 2018

Papers citing "Bypassing Feature Squeezing by Increasing Adversary Strength"

7 / 7 papers shown

Title
Adversarial Sampling for Fairness Testing in Deep Neural Network Tosin Ige William Marfo Justin Tonkinson Sikiru Adewale Bolanle Hafiz Matti OOD 26 9 0 06 Mar 2023
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 28 2 0 31 Jul 2022
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks Yulong Cao* Ningfei Wang* Chaowei Xiao Dawei Yang Jin Fang Ruigang Yang Qi Alfred Chen Mingyan D. Liu Bo-wen Li AAML 29 219 0 17 Jun 2021
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs Lorena Qendro Sangwon Ha R. D. Jong Partha P. Maji AAML FedML MQ 21 7 0 13 May 2021
Scratch that! An Evolution-based Adversarial Attack against Neural Networks Malhar Jere Loris Rossi Briland Hitaj Gabriela F. Cretu-Ciocarlie Giacomo Boracchi F. Koushanfar AAML 14 18 0 05 Dec 2019
CAAD 2018: Generating Transferable Adversarial Examples Yash Sharma Tien-Dung Le M. Alzantot AAML SILM 26 7 0 29 Sep 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 298 3,115 0 04 Nov 2016