Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods
Authors: Arif Siddiqi | Published: 2019-07-17
ポイズニング
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Authors: Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica | Published: 2019-07-16 | Updated: 2019-09-03
Stateful Detection of Black-Box Adversarial Attacks
Authors: Steven Chen, Nicholas Carlini, David Wagner | Published: 2019-07-12
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Authors: Francesco Croce, Matthias Hein | Published: 2019-07-03 | Updated: 2020-07-20
Comment on “Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network”
Authors: Roland S. Zimmermann | Published: 2019-07-01
Improving Black-box Adversarial Attacks with a Transfer-based Prior
Authors: Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu | Published: 2019-06-17 | Updated: 2020-07-26
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Authors: Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sebastien Bubeck | Published: 2019-06-09 | Updated: 2020-01-10
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
Authors: Walt Woods, Jack Chen, Christof Teuscher | Published: 2019-06-07 | Updated: 2019-08-06
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Authors: Pantelis Elinas, Edwin V. Bonilla, Louis Tiao | Published: 2019-06-05 | Updated: 2020-10-21
Unlabeled Data Improves Adversarial Robustness
Authors: Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi | Published: 2019-05-31 | Updated: 2022-01-13