Differentially Private Adversarial Robustness Through Randomized Perturbations
Authors: Nan Xu, Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier | Published: 2020-09-27
敵対的サンプル
Adversarial Examples in Deep Learning for Multivariate Time Series Regression
Authors: Gautam Raj Mode, Khaza Anuarul Hoque | Published: 2020-09-24
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
Authors: Prashnna Kumar Gyawali, Sandesh Ghimire, Linwei Wang | Published: 2020-09-23
Feature Distillation With Guided Adversarial Contrastive Learning
Authors: Tao Bai, Jinnan Chen, Jun Zhao, Bihan Wen, Xudong Jiang, Alex Kot | Published: 2020-09-21
Adversarial Training with Stochastic Weight Average
Authors: Joong-Won Hwang, Youngwan Lee, Sungchan Oh, Yuseok Bae | Published: 2020-09-21
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
Authors: Yaguan Qian, Qiqi Shao, Jiamin Wang, Xiang Lin, Yankai Guo, Zhaoquan Gu, Bin Wang, Chunming Wu | Published: 2020-09-19 | Updated: 2020-11-25
Robust Deep Learning Ensemble against Deception
Authors: Wenqi Wei, Ling Liu | Published: 2020-09-14
Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks
Authors: Shankar A. Deka, Dušan M. Stipanović, Claire J. Tomlin | Published: 2020-09-07
Detection Defense Against Adversarial Attacks with Saliency Map
Authors: Dengpan Ye, Chuanxi Chen, Changrui Liu, Hao Wang, Shunzhi Jiang | Published: 2020-09-06
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Authors: Han Xu, Yaxin Li, Xiaorui Liu, Hui Liu, Jiliang Tang | Published: 2020-09-02