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We study the transfer of adversarial robustness of deep neural networks between different perturbation types.
On evaluating adversarial robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I. J., Madry, A., and Kurakin, A · 1902
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An algorithm for quadratic programming
Frank, M. and Wolfe, P · 1956
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On Cezari’s convergence of the steepest descent method for approximating saddle point of convex-concave functions
Nemirovski, A. and Yudin, D · 1978
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Problem Complexity and Method Efficiency in Optimization
Nemirovski, A. and Yudin, D · 1983
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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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.
Synthesizing robust adversarial examples
Athalye, A., Engstrom, L., Ilyas, A., and Kwok, K · 2017
Cited alongside, same era.
Brown, T. B., Mané, D., Roy, A., Abadi, M., and Gilmer, J · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
A rotation and a translation suffice: Fooling CNNs with simple transformations
Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A · 2017
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JPEG-resistant adversarial images
Shin, R. and Song, D · 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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EAD: Elastic-net attacks to deep neural networks via adversarial examples
Chen, P.-Y., Sharma, Y., Zhang, H., Yi, J., and Hsieh, C.-J · 2018
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Adversarial generative nets: Neural network attacks on state-of-the-art face recognition
Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M. K · 2018
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Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D · 2018
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Accurate, large minibatch SGD: Training Imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Attacking the Madry Defense Model with L 1 L_{1} -based Adversarial Examples
Sharma, Y. and Chen, P.-Y · 2017
Cited alongside, same era.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2018
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Quantifying Perceptual Distortion of Adversarial Examples
Jordan, M., Manoj, N., Goel, S., and Dimakis, A. G · 2019
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Adversarial Training and Robustness for Multiple Perturbations
Tramèr, F. and Boneh, D · 2019
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