Using Randomness to Improve Robustness of Machine-Learning Models
Against Evasion Attacks

Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks

10 August 2018

Fan Yang

Papers citing "Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks"

5 / 5 papers shown

Title
On the vulnerability of fingerprint verification systems to fake fingerprint attacks J. Galbally-Herrero Julian Fierrez-Aguilar J.D. Rodriguez-Gonzalez F. Alonso-Fernandez J. Ortega-Garcia M. Tapiador 26 83 0 11 Jul 2022
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 118 1,772 0 22 Aug 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno D. Song AAML 50 595 0 27 Jul 2017
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 102 1,805 0 09 Sep 2016
Adversarial Perturbations Against Deep Neural Networks for Malware Classification Kathrin Grosse Nicolas Papernot Praveen Manoharan Michael Backes Patrick McDaniel AAML 64 418 0 14 Jun 2016