Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

8 December 2017

Battista Biggio

Fabio Roli

Papers citing "Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning"

10 / 210 papers shown

Title
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 20 394 0 30 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar A. Madry OOD AAML 25 784 0 30 Apr 2018
Adversarial Attacks Against Medical Deep Learning Systems S. G. Finlayson Hyung Won Chung I. Kohane Andrew L. Beam SILM AAML OOD MedIm 22 229 0 15 Apr 2018
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables Bojan Kolosnjaji Ambra Demontis Battista Biggio Davide Maiorca Giorgio Giacinto Claudia Eckert Fabio Roli AAML 22 316 0 12 Mar 2018
Explaining Black-box Android Malware Detection Marco Melis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 9 43 0 09 Mar 2018
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware Abdullah Al-Dujaili Alex Huang Erik Hemberg Una-May O’Reilly AAML 25 186 0 09 Jan 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 13 191 0 31 Dec 2017
Hardening Quantum Machine Learning Against Adversaries N. Wiebe Ramnath Kumar AAML 25 20 0 17 Nov 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016