Comment on “Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network”
Authors: Roland S. Zimmermann | Published: 2019-07-01
Poisoning
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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
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Authors: Andrey Malinin, Mark Gales | Published: 2019-05-31 | Updated: 2019-12-02
Bandlimiting Neural Networks Against Adversarial Attacks
Authors: Yuping Lin, Kasra Ahmadi K. A., Hui Jiang | Published: 2019-05-30
An Investigation of Data Poisoning Defenses for Online Learning
Authors: Yizhen Wang, Somesh Jha, Kamalika Chaudhuri | Published: 2019-05-28 | Updated: 2020-02-19
Certifiably Robust Interpretation in Deep Learning
Authors: Alexander Levine, Sahil Singla, Soheil Feizi | Published: 2019-05-28 | Updated: 2019-10-17