ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness
Authors: Anindya Sarkar, Anirudh Sunder Raj, Raghu Sesha Iyengar | Published: 2019-10-15 | Updated: 2020-09-27
対抗的学習
Partially Encrypted Machine Learning using Functional Encryption
Authors: Theo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach, David Pointcheval | Published: 2019-05-24 | Updated: 2021-09-23
Learning More Robust Features with Adversarial Training
Authors: Shuangtao Li, Yuanke Chen, Yanlin Peng, Lin Bai | Published: 2018-04-20
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Authors: Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord, Pushmeet Kohli | Published: 2018-02-15 | Updated: 2018-06-12
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Authors: Xiaoyu Cao, Neil Zhenqiang Gong | Published: 2017-09-17 | Updated: 2019-12-31
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Authors: Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh | Published: 2017-09-13 | Updated: 2018-02-10
Towards Proving the Adversarial Robustness of Deep Neural Networks
Authors: Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer | Published: 2017-09-08
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Authors: Yi Han, Benjamin I. P. Rubinstein | Published: 2017-04-06 | Updated: 2017-05-25
Comment on “Biologically inspired protection of deep networks from adversarial attacks”
Authors: Wieland Brendel, Matthias Bethge | Published: 2017-04-05