Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning
Authors: Andrew P. Norton, Yanjun Qi | Published: 2017-08-01
Literature Database
Quantum Privacy-Preserving Perceptron
Authors: Shenggang Ying, Mingsheng Ying, Yuan Feng | Published: 2017-07-31
Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data
Authors: Benjamin L. Bullough, Anna K. Yanchenko, Christopher L. Smith, Joseph R. Zipkin | Published: 2017-07-25
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
Authors: Artur Filipowicz, Thee Chanyaswad, S. Y. Kung | Published: 2017-07-24
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
Authors: Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici | Published: 2017-07-19 | Updated: 2018-06-24
Houdini: Fooling Deep Structured Prediction Models
Authors: Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet | Published: 2017-07-17
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Authors: Jonas Rauber, Wieland Brendel, Matthias Bethge | Published: 2017-07-13 | Updated: 2018-03-20
Process Monitoring on Sequences of System Call Count Vectors
Authors: Michael Dymshits, Ben Myara, David Tolpin | Published: 2017-07-12
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
Authors: Jiajun Lu, Hussein Sibai, Evan Fabry, David Forsyth | Published: 2017-07-12
A Survey on Resilient Machine Learning
Authors: Atul Kumar, Sameep Mehta | Published: 2017-07-11