The Limitations of Model Uncertainty in Adversarial Settings

Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization

Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal · 1997

Earlier work this paper cites.

Gradient-based learning applied to document recognition

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998

Earlier work this paper cites.

SciPy: Open source scientific tools for Python, 2001

Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2001

Earlier work this paper cites.

Good word attacks on statistical spam filters

Daniel Lowd and Christopher Meek · 2005

Earlier work this paper cites.

GPy: A gaussian process framework in python

GPy · 2012

Earlier work this paper cites.

UCI machine learning repository, 2013

M. Lichman · 2013

Earlier work this paper cites.

Using machine teaching to identify optimal training-set attacks on machine learners

Shike Mei and Xiaojin Zhu · 2015

Earlier work this paper cites.

Explaining and harnessing adversarial examples

Ian J Goodfellow et al · 2015

Earlier work this paper cites.

Deepfool: A simple and accurate method to fool deep neural networks

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016

Cited alongside, same era.

Tensorflow: a system for large-scale machine learning

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016

Cited alongside, same era.

Transferability in machine learning: from phenomena to black-box attacks using adversarial samples

Original

Nicolas Papernot, Patrick McDaniel, and Ian J. Goodfellow · 2016

Cited alongside, same era.

Multiplicative normalizing flows for variational bayesian neural networks

Christos Louizos and Max Welling · 2017

Cited alongside, same era.

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning

A. Rawat, M. Wistuba, and M.-I. Nicolae · 2017

Cited alongside, same era.

Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms

Original

Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017

Later among the works it cites.

Wild patterns: Ten years after the rise of adversarial machine learning

Battista Biggio and Fabio Roli · 2018

Closest in time.

Bayesian adversarial spheres: Bayesian inference and adversarial examples in a noiseless setting

Artur Bekasov and Iain Murray · 2018

Closest in time.

Idealised bayesian neural networks cannot have adversarial examples: Theoretical and empirical study

Original

Yarin Gal and Lewis Smith · 2018

Closest in time.

Understanding measures of uncertainty for adversarial example detection

Lewis Smith and Yarin Gal · 2018

Closest in time.