MEADE: Towards a Malicious Email Attachment Detection Engine
Authors: Ethan M. Rudd, Richard Harang, Joshua Saxe | Published: 2018-04-22
敵対的サンプルの検知
On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples
Authors: Pei-Hsuan Lu, Pin-Yu Chen, Kang-Cheng Chen, Chia-Mu Yu | Published: 2018-04-14 | Updated: 2018-05-09
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Authors: Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu | Published: 2018-03-26
Clipping free attacks against artificial neural networks
Authors: Boussad Addad, Jerome Kodjabachian, Christophe Meyer | Published: 2018-03-26 | Updated: 2018-03-28
Detecting Adversarial Examples – A Lesson from Multimedia Forensics
Authors: Pascal Schöttle, Alexander Schlögl, Cecilia Pasquini, Rainer Böhme | Published: 2018-03-09
Stochastic Activation Pruning for Robust Adversarial Defense
Authors: Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar | Published: 2018-03-05
On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
Authors: Mahmood Sharif, Lujo Bauer, Michael K. Reiter | Published: 2018-02-27 | Updated: 2018-07-27
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning
Authors: Christopher Frederickson, Michael Moore, Glenn Dawson, Robi Polikar | Published: 2018-02-20
The Robust Manifold Defense: Adversarial Training using Generative Models
Authors: Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis, Alexandros G. Dimakis | Published: 2017-12-26 | Updated: 2019-07-10
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
Authors: Yash Sharma, Pin-Yu Chen | Published: 2017-10-30 | Updated: 2018-07-27