Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples
Authors: Dan Peng, Zizhan Zheng, Xiaofeng Zhang | Published: 2018-09-08 | Updated: 2018-12-22
敵対的サンプルの検知
Are adversarial examples inevitable?
Authors: Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein | Published: 2018-09-06 | Updated: 2020-02-03
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
Authors: Zirui Xu, Fuxun Yu, Chenchen Liu, Xiang Chen | Published: 2018-09-04
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks
Authors: Siwakorn Srisakaokul, Yuhao Zhang, Zexuan Zhong, Wei Yang, Tao Xie, Bo Li | Published: 2018-08-31 | Updated: 2019-07-27
Security and Privacy Issues in Deep Learning
Authors: Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, Sungroh Yoon | Published: 2018-07-31 | Updated: 2021-03-10
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Authors: Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin | Published: 2018-07-10 | Updated: 2018-10-27
Detection based Defense against Adversarial Examples from the Steganalysis Point of View
Authors: Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, Nenghai Yu | Published: 2018-06-21 | Updated: 2018-12-24
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Authors: Chirag Agarwal, Bo Dong, Dan Schonfeld, Anthony Hoogs | Published: 2018-06-05 | Updated: 2018-06-06
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Authors: Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas | Published: 2018-05-31
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Authors: Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng | Published: 2018-05-30 | Updated: 2020-01-31