Evaluating Explanation Methods for Deep Learning in Security
Authors: Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck | Published: 2019-06-05 | Updated: 2020-04-27
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
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