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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
Original
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Bastiaan Kleijn, W., Zhang, M., and Balduzzi, D · 2015
Earlier work this paper cites.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
Earlier work this paper cites.
A rotation and a translation suffice: Fooling cnns with simple transformations
Original
Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Original
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Earlier work this paper cites.

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2019

MNIST-C: A Robustness Benchmark for Computer Vision

Mu, Norman, Gilmer, Justin

Understand

We introduce the MNIST-C dataset, a comprehensive suite of 15 corruptions applied to the MNIST test set, for benchmarking out-of-distribution robustness in computer vision.

Through several experiments and visualizations we demonstrate that our corruptions significantly degrade performance of state-of-the-art computer vision models while preserving the semantic content of the test images.
In contrast to the popular notion of adversarial robustness, our model-agnostic corruptions do not seek worst-case performance but are instead designed to be broad and diverse, capturing multiple failure modes of modern models.
In fact, we find that several previously published adversarial defenses significantly degrade robustness as measured by MNIST-C.

Built on

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
Original
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Bastiaan Kleijn, W., Zhang, M., and Balduzzi, D · 2015
Earlier work this paper cites.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
Earlier work this paper cites.
A rotation and a translation suffice: Fooling cnns with simple transformations
Original
Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Original
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Earlier work this paper cites.