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State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data.
Signature verification using a ”siamese” time delay neural network
Bromley, J., W. Bentz, J., Bottou, L., Guyon, I., Lecun, Y., Moore, C., Sackinger, E., and Shah, R · 1993
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A fast iterative nearest point algorithm for support vector machine classifier design
Keerthi, S., Shevade, S. K., Bhattacharyya, C., and Murthy, K · 1999
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., and LeCun, Y · 2005
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Online learning of maximum p-norm margin classifiers with bias
Ishibashi, K., Hatano, K., and Takeda, M · 2008
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The elements of statistical learning: data mining, inference and prediction
Hastie, T., Tibshirani, R., and Friedman, J · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., and Salakhutdinov, R · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M. J · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Martin, C. H. and Mahoney, M. W · 2018
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Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
Marzi, Z., Gopalakrishnan, S., Madhow, U., and Pedarsani, R · 2018
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The robustness of deep networks: A geometrical perspective
Fawzi, A., Moosavi-Dezfooli, S., and Frossard, P · 2017
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Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Cited alongside, same era.
Foolbox: a python toolbox to benchmark the robustness of machine learning models (2017)
Rauber, J., Brendel, W., and Bethge, M · 2017
Cited alongside, same era.
Step size matters in deep learning
Nar, K. and Sastry, S · 2018
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Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
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The Implicit Bias of Gradient Descent on Separable Data
Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., and Srebro, N · 2018
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