Understanding and Quantifying Adversarial Examples Existence in Linear Classification
Authors: Xupeng Shi, A. Adam Ding | Published: 2019-10-27
Detection of Adversarial Attacks and Characterization of Adversarial Subspace
Authors: Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich | Published: 2019-10-26
Automatic Driver Identification from In-Vehicle Network Logs
Authors: Mina Remeli, Szilvia Lestyan, Gergely Acs, Gergely Biczok | Published: 2019-10-25
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Authors: Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein | Published: 2019-10-25
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
Authors: Mathieu N Galtier, Camille Marini | Published: 2019-10-25
Neurlux: Dynamic Malware Analysis Without Feature Engineering
Authors: Chani Jindal, Christopher Salls, Hojjat Aghakhani, Keith Long, Christopher Kruegel, Giovanni Vigna | Published: 2019-10-24
A Note on Our Submission to Track 4 of iDASH 2019
Authors: Marcel Keller, Ke Sun | Published: 2019-10-24
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique
Authors: Muhammad Furqan Rafique, Muhammad Ali, Aqsa Saeed Qureshi, Asifullah Khan, Anwar Majid Mirza | Published: 2019-10-24 | Updated: 2020-12-26
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Authors: Wei Deng, Xiao Zhang, Faming Liang, Guang Lin | Published: 2019-10-23 | Updated: 2020-04-13
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Authors: Alexander Levine, Soheil Feizi | Published: 2019-10-23