Killing four birds with one Gaussian process: the relation between
different test-time attacks

v1v2v3 (latest)

Killing four birds with one Gaussian process: the relation between different test-time attacks

6 June 2018

Michael Backes

ArXiv (abs)PDF HTML

Papers citing "Killing four birds with one Gaussian process: the relation between different test-time attacks"

15 / 15 papers shown

Title
The Limitations of Model Uncertainty in Adversarial Settings Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 44 34 0 06 Dec 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 87 251 0 23 Feb 2018
DARTS: Deceiving Autonomous Cars with Toxic Signs Chawin Sitawarin A. Bhagoji Arsalan Mosenia M. Chiang Prateek Mittal AAML 91 235 0 18 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 224 3,186 0 01 Feb 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 128 1,093 0 01 Nov 2017
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization Luis Muñoz-González Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C. Lupu Fabio Roli AAML 99 633 0 29 Aug 2017
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid Marco Melis Ambra Demontis Battista Biggio Gavin Brown Giorgio Fumera Fabio Roli AAML 52 98 0 23 Aug 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 157 2,153 0 21 Aug 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 103 171 0 08 Jul 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 126 1,864 0 20 May 2017
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection Ambra Demontis Marco Melis Battista Biggio Davide Maiorca Dan Arp Konrad Rieck Igino Corona Giorgio Giacinto Fabio Roli AAML 55 284 0 28 Apr 2017
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Briland Hitaj G. Ateniese Fernando Perez-Cruz FedML 117 1,404 0 24 Feb 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 107 1,807 0 09 Sep 2016
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 112 1,593 0 27 Jun 2012