Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Authors: Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu | Published: 2019-03-05 | Updated: 2019-05-22
モデルの頑健性保証
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN
Authors: Ke Sun, Zhanxing Zhu, Zhouchen Lin | Published: 2019-02-28
Tensor Dropout for Robust Learning
Authors: Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Anima Anandkumar, Ioanna Tzoulaki, Paul Matthews | Published: 2019-02-27 | Updated: 2020-12-11
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Authors: Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang | Published: 2019-02-23 | Updated: 2020-01-10
Quantifying Perceptual Distortion of Adversarial Examples
Authors: Matt Jordan, Naren Manoj, Surbhi Goel, Alexandros G. Dimakis | Published: 2019-02-21
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Authors: Eric Wong, Frank R. Schmidt, J. Zico Kolter | Published: 2019-02-21 | Updated: 2020-01-18
There are No Bit Parts for Sign Bits in Black-Box Attacks
Authors: Abdullah Al-Dujaili, Una-May O'Reilly | Published: 2019-02-19 | Updated: 2019-04-03
On Evaluating Adversarial Robustness
Authors: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin | Published: 2019-02-18 | Updated: 2019-02-20
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Authors: Kevin Roth, Yannic Kilcher, Thomas Hofmann | Published: 2019-02-13 | Updated: 2019-05-09
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
Authors: Priyadarshini Panda, Indranil Chakraborty, Kaushik Roy | Published: 2019-02-08 | Updated: 2019-02-11