Handling Adversarial Concept Drift in Streaming Data

24 March 2018

Papers citing "Handling Adversarial Concept Drift in Streaming Data"

11 / 11 papers shown

Title
Security Theater: On the Vulnerability of Classifiers to Exploratory Attacks Tegjyot Singh Sethi M. Kantardzic J. Ryu AAML 72 11 0 24 Mar 2018
Security Evaluation of Pattern Classifiers under Attack Battista Biggio Giorgio Fumera Fabio Roli AAML 62 443 0 02 Sep 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 155 2,147 0 21 Aug 2017
On the Reliable Detection of Concept Drift from Streaming Unlabeled Data Tegjyot Singh Sethi M. Kantardzic 33 177 0 31 Mar 2017
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains Tegjyot Singh Sethi M. Kantardzic AAML 88 49 0 23 Mar 2017
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking Erwin Quiring Dan Arp Konrad Rieck AAML 49 6 0 16 Mar 2017
Towards the Science of Security and Privacy in Machine Learning Nicolas Papernot Patrick McDaniel Arunesh Sinha Michael P. Wellman AAML 77 474 0 11 Nov 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 102 1,804 0 09 Sep 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 112 1,740 0 24 May 2016
Exponentially Weighted Moving Average Charts for Detecting Concept Drift Gordon J. Ross N. Adams D. Tasoulis D. Hand 68 371 0 25 Dec 2012
Learning under Concept Drift: an Overview Indrė Žliobaitė 84 454 0 22 Oct 2010