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
文献データベース
PAC-learning in the presence of evasion adversaries
Authors: Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal | Published: 2018-06-05 | Updated: 2018-06-06
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise
Authors: Vahid Behzadan, Arslan Munir | Published: 2018-06-04
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Authors: Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, Michael Backes | Published: 2018-06-04 | Updated: 2018-12-14
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
Authors: Yarin Gal, Lewis Smith | Published: 2018-06-02 | Updated: 2018-06-28
Detecting Adversarial Examples via Key-based Network
Authors: Pinlong Zhao, Zhouyu Fu, Ou wu, Qinghua Hu, Jun Wang | Published: 2018-06-02
Tokenized Data Markets
Authors: Bharath Ramsundar, Roger Chen, Alok Vasudev, Rob Robbins, Artur Gorokh | Published: 2018-05-31
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Authors: Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas | Published: 2018-05-31
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Authors: Partha Ghosh, Arpan Losalka, Michael J Black | Published: 2018-05-31 | Updated: 2018-12-10
Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations
Authors: Taesung Lee, Benjamin Edwards, Ian Molloy, Dong Su | Published: 2018-05-31 | Updated: 2018-12-13