Natural and Adversarial Error Detection using Invariance to Image Transformations

Information theory and statistics

Kullback, S · 1959

Earlier work this paper cites.

Learning multiple layers of features from tiny images

Krizhevsky, A. and Hinton, G · 2009

Earlier work this paper cites.

An analysis of single-layer networks in unsupervised feature learning

Coates, A., Ng, A., and Lee, H · 2011

Earlier work this paper cites.

Intriguing properties of neural networks

Original

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013

Earlier work this paper cites.

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014

Earlier work this paper cites.

Distilling the knowledge in a neural network

Original

Hinton, G., Vinyals, O., and Dean, J · 2015

Earlier work this paper cites.

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., and Clune, J · 2015

Earlier work this paper cites.

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015

Earlier work this paper cites.

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Hendrycks, D. and Gimpel, K · 2016

Cited alongside, same era.

Early methods for detecting adversarial images

Hendrycks, D. and Gimpel, K · 2016

Cited alongside, same era.

Distillation as a defense to adversarial perturbations against deep neural networks

Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2016

Cited alongside, same era.

Inception-v4, inception-resnet and the impact of residual connections on learning

Szegedy, C., Ioffe, S., and Vanhoucke, V · 2016

Cited alongside, same era.

Dimensionality reduction as a defense against evasion attacks on machine learning classifiers

Bhagoji, A. N., Cullina, D., and Mittal, P · 2017

Cited alongside, same era.

Towards evaluating the robustness of neural networks

Carlini, N. and Wagner, D. A · 2017

On the (statistical) detection of adversarial examples

Original

Grosse, K., Manoharan, P., Papernot, N., Backes, M., and McDaniel, P · 2017

Later among the works it cites.

Densely connected convolutional networks

Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L · 2017

Later among the works it cites.

Adversarial examples detection in deep networks with convolutional filter statistics

Li, X. and Li, F · 2017

Later among the works it cites.

Towards deep learning models resistant to adversarial attacks

Original

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017

Later among the works it cites.

Distance-based Confidence Score for Neural Network Classifiers

Mandelbaum, A. and Weinshall, D · 2017

Later among the works it cites.