Adversarial Training for Free!
Authors: Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein | Published: 2019-04-29 | Updated: 2019-11-20
ポイズニング
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
Authors: David J. Miller, Zhen Xiang, George Kesidis | Published: 2019-04-12 | Updated: 2019-12-02
Online Data Poisoning Attack
Authors: Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard | Published: 2019-03-05 | Updated: 2019-05-30
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Authors: Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, Nam Ik Cho | Published: 2019-03-02
Attacking Graph-based Classification via Manipulating the Graph Structure
Authors: Binghui Wang, Neil Zhenqiang Gong | Published: 2019-03-01 | Updated: 2019-08-13
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Authors: Panagiota Kiourti, Kacper Wardega, Susmit Jha, Wenchao Li | Published: 2019-03-01
Identifying Malicious Web Domains Using Machine Learning Techniques with Online Credibility and Performance Data
Authors: Zhongyi Hu, Raymond Chiong, Ilung Pranata, Willy Susilo, Yukun Bao | Published: 2019-02-23
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Authors: Gavin Weiguang Ding, Luyu Wang, Xiaomeng Jin | Published: 2019-02-20
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Authors: Silvija Kokalj-Filipovic, Rob Miller, Nicholas Chang, Chi Leung Lau | Published: 2019-02-16
Adversarial Attack and Defense on Graph Data: A Survey
Authors: Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Yixin Liu, Philip S. Yu, Lifang He, Bo Li | Published: 2018-12-26 | Updated: 2022-10-06