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This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks.
CapsAttacks: Robust and imperceptible adversarial attacks on capsule networks
Marchisio, A., Nanfa, G., Khalid, F., Hanif, M. A., Martina, M., and Shafique, M · 1901
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Reading digits in natural images with unsupervised feature Learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Deepfool: A simple and accurate method to fool deep neural Networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
Capsule network performance on complex data
Xi, E., Bing, S., and Jin, Y · 2017
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Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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DARCCC: Detecting adversaries by reconstruction from class conditional capsules
Frosst, N., Sabour, S., and Hinton, G · 2018
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Matrix capsules with em routing
Hinton, G. E., Sabour, S., and Frosst, N · 2018
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Training deep capsule networks
Peer, D., Stabinger, S., and Rodríguez-Sánchez, A. J · 2018
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Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Cited alongside, same era.
Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
Cited alongside, same era.
Dense and diverse capsule networks: Making the capsules learn better
Phaye, S. S. R., Sikka, A., Dhall, A., and Bathula, D. R · 2018
Later among the works it cites.