Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks

5 April 2019

Papers citing "Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks"

8 / 8 papers shown

Title
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry M. Penrod Harrison Termotto Varshini Reddy Jiayu Yao Finale Doshi-Velez Weiwei Pan AAML OOD 40 1 0 02 Aug 2022
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review Florian Tambon Gabriel Laberge Le An Amin Nikanjam Paulina Stevia Nouwou Mindom Y. Pequignot Foutse Khomh G. Antoniol E. Merlo François Laviolette 30 66 0 26 Jul 2021
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks Thai Le Noseong Park Dongwon Lee 10 23 0 20 Nov 2020
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Bo-wen Li P. Varshney D. Song AAML 28 47 0 16 Mar 2020
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks K. Sivamani R. Sahay Aly El Gamal AAML 6 3 0 22 Feb 2020
Test Selection for Deep Learning Systems Wei Ma Mike Papadakis Anestis Tsakmalis Maxime Cordy Yves Le Traon OOD 21 91 0 30 Apr 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 197 745 0 06 Jun 2015