Adversarial Training is a Form of Data-dependent Operator Norm Regularization

Authors: Kevin Roth, Yannic Kilcher, Thomas Hofmann | Published: 2019-06-04 | Updated: 2020-10-23

DAWN: Dynamic Adversarial Watermarking of Neural Networks

Authors: Sebastian Szyller, Buse Gul Atli, Samuel Marchal, N. Asokan | Published: 2019-06-03 | Updated: 2021-07-16

Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models

Authors: Paweł Morawiecki, Przemysław Spurek, Marek Śmieja, Jacek Tabor | Published: 2019-06-03 | Updated: 2019-07-03

Disparate Vulnerability to Membership Inference Attacks

Authors: Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, Carmela Troncoso | Published: 2019-06-02 | Updated: 2021-09-16

SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

Authors: Qian Lou, Lei Jiang | Published: 2019-06-01 | Updated: 2019-11-16

IoT Network Security from the Perspective of Adversarial Deep Learning

Authors: Yalin E. Sagduyu, Yi Shi, Tugba Erpek | Published: 2019-05-31

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

Are Labels Required for Improving Adversarial Robustness?

Authors: Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli | Published: 2019-05-31 | Updated: 2019-12-05

L0 Regularization Based Neural Network Design and Compression

Authors: S. Asim Ahmed | Published: 2019-05-31

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