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Many defenses have emerged with the development of adversarial attacks.
“MixConv: Mixed Depthwise Convolutional Kernels,”
Mingxing Tan and Quoc V. Le, · 1907
Earlier work this paper cites.
“ImageNet: A Large-Scale Hierarchical Image Database,”
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
A. Krizhevsky and G. Hinton, · 2009
Earlier work this paper cites.
“MNIST handwritten digit database,”
Y. LeCun and C. Cortes, · 2010
Earlier work this paper cites.
“Torchvision the machine-vision package of torch,”
S. Marcel and Y. Rodriguez, · 2010
Earlier work this paper cites.
“One weird trick for parallelizing CNN,”
A. Krizhevsky, · 2014
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 2014
Earlier work this paper cites.
“Explaining and harnessing adversarial examples,”
I. J. Goodfellow, J. Shlens, and C. Szegedy, · 2015
Earlier work this paper cites.
“Deepfool: A simple and accurate method to fool deep neural networks,”
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Adversarial examples in the physical world,”
A. Kurakin, I. J. Goodfellow, and S. Bengio, · 2017
Earlier work this paper cites.
“Towards evaluating the robustness of neural networks,”
Nicholas Carlini and David Wagner, · 2017
Earlier work this paper cites.
“Dual path networks,”
Y. Chenand J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, · 2017
Cited alongside, same era.
“Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,”
W. Brendel, J. Rauber, and M. Bethge, · 2018
Cited alongside, same era.
“Towards deep learning models resistant to adversarial attacks,”
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, · 2018
Cited alongside, same era.
“Mobilenetv2: Inverted residuals and linear bottlenecks,”
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen, · 2018
Cited alongside, same era.
“Squeeze-and-excitation networks,”
Jie Hu, Li Shen, and Gang Sun, · 2018
Cited alongside, same era.
“EfficientNet: Rethinking model scaling for CNN,”
M. Tan and Q. Le, · 2019
Cited alongside, same era.
“Qeba: Query-efficient boundary-based blackbox attack,”
H. Li, X. Xu, X. Zhang, S. Yang, and B. Li, · 2020
Later among the works it cites.
“Geoda: a geometric framework for black-box adversarial attacks,”
A. Rahmati, S.-M. Moosavi-Dezfooli, P. Frossard, and H. Dai, · 2020
Later among the works it cites.
“Square attack: a query-efficient black-box adversarial attack via random search,”
M. Andriushchenko, F. Croce, N. Flammarion, and M. Hein, · 2020
Later among the works it cites.
“Surfree: a fast surrogate-free black-box attack,”
Thibault Maho, Teddy Furon, and Erwan Le Merrer, · 2020
Later among the works it cites.
“What if adversarial samples were digital images?,”
B. Bonnet, T. Furon, and P. Bas, · 2020
Later among the works it cites.
“CSPNet: A new backbone that can enhance learning capability of CNN,”
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“Pytorch image models,” https://github.com/rwightman/pytorch-image-models
Ross Wightman, · 2019
Cited alongside, same era.
“Robustbench: a standardized adversarial robustness benchmark,”
F. Croce, M. Andriushchenko, V. Sehwag, N. Flammarion, M. Chiang, P. Mittal, and M. Hein, · 2020
Cited alongside, same era.
“Benchmarking adversarial robustness on image classification,”
Y. Dong, Q.-A. Fu, X. Yang, T. Pang, H. Su, Z. Xiao, and J. Zhu, · 2020
Cited alongside, same era.
“Rays: A ray searching method for hard-label adversarial attack,”
J. Chen and Q. Gu, · 2020
Cited alongside, same era.
“HopSkipJumpAttack: A query-efficient decision-based attack,”
J. Chen, M. I. Jordan, and M. J. Wainwright, · 2020
Cited alongside, same era.
“Robust vision benchmark,”
Bethge Lab,
Cited in the paper.
C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I-H. Yeh, · 2020
Later among the works it cites.
“ReXNet: Diminishing Representational Bottleneck on CNN,”
D. Han, S. Yun, B. Heo, and Y. Yoo, · 2020
Later among the works it cites.
“Designing network design spaces,”
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár, · 2020
Later among the works it cites.
“Adversarial examples improve image recognition,”
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le, · 2020
Later among the works it cites.
“Self-training with noisy student improves imagenet classification,”
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le, · 2020
Later among the works it cites.
“Walking on the edge: Fast, low-distortion adversarial examples,”
H. Zhang, Y. Avrithis, T. Furon, and L. Amsaleg, · 2021
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