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The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood.
Differential Geometry of Curves and Surfaces
do Carmo, M.: · 1976
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
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Compositional pattern producing networks: A novel abstraction of development
Stanley, K.O.: · 2007
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Network in network
Lin, M., Chen, Q., Yan, S.: · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedald, A., Zisserman, A.: · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., Szegedy, C.: · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J.: · 2015
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Improving the adversarial robustness of convnets by reduction of input dimensionality (2015)
Maharaj, A.V.: · 2015
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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., Fei-Fei, L.: · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: · 2016
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Adversarial manipulation of deep representations
Sabour*, S., Cao*, Y., Faghri, F., Fleet, D.J.: · 2016
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A theoretical framework for robustness of (deep) classifiers under adversarial noise
Wang, B., Gao, J., Qi, Y.: · 2016
Cited alongside, same era.
A boundary tilting persepective on the phenomenon of adversarial examples
Tanay, T., Griffin, L.: · 2016
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Mitigating adversarial effects through randomization
Xie, C., Wang, J., Zhang, Z., Ren, Z., Yuille, A.L.: · 2017
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Safetynet: Detecting and rejecting adversarial examples robustly
Lu, J., Issaranon, T., Forsyth, D.: · 2017
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On detecting adversarial perturbations
Metzen, J.H., Genewein, T., Fischer, V., Bischoff, B.: · 2017
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Classification regions of deep neural networks
Fawzi*, A., Moosavi-Dezfooli*, S.M., Frossard, P., Soatto, S.: · 2017
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The robustness of deep networks: A geometrical perspective
Fawzi, A., Moosavi-Dezfooli, S.M., Frossard, P.: · 2017
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Robustness of classifiers: from adversarial to random noise
Fawzi*, A., Moosavi-Dezfooli*, S.M., Frossard, P.: · 2016
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli*, S.M., Fawzi*, A., Fawzi, O., Frossard, P.: · 2017
Cited alongside, same era.
Deepcloak: Masking deep neural network models for robustness against adversarial samples
Gao, J., Wang, B., Lin, Z., Xu, W., Qi, Y.: · 2017
Cited alongside, same era.
Keeping the bad guys out: Protecting and vaccinating deep learning with JPEG compression
Das, N., Shanbhogue, M., Chen, S., Hohman, F., Chen, L., Kounavis, M.E., Chau, D.H.: · 2017
Cited alongside, same era.
2017 competition on adversarial attacks and defenses
NIPS: · 2018
Closest in time.
Robustness of classifiers to universal perturbations: A geometric perspective
Moosavi-Dezfooli*, S.M., Fawzi*, A., Fawzi, O., Frossard, P., Soatto, S.: · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y., Lopez-Paz, D.: · 2018
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