An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Authors: Chirag Agarwal, Bo Dong, Dan Schonfeld, Anthony Hoogs | Published: 2018-06-05 | Updated: 2018-06-06
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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Authors: Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas | Published: 2018-05-31
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Authors: Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng | Published: 2018-05-30 | Updated: 2020-01-31
MEADE: Towards a Malicious Email Attachment Detection Engine
Authors: Ethan M. Rudd, Richard Harang, Joshua Saxe | Published: 2018-04-22
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