RAIN: A Simple Approach for Robust and Accurate Image Classification Networks

R. G. Keys, “Cubic convolution interpolation for digital image processing,” 1981

1981

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2009, pp. 248–255

2009

Earlier work this paper cites.

A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , 2011, pp. 215–223

2011

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013

Original

2013

Earlier work this paper cites.

B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2013

2013

Earlier work this paper cites.

Y. Sun, X. Wang, and X. Tang, “Deep learning face representation from predicting 10,000 classes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 1891–1898

2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014

Original

2014

Earlier work this paper cites.

Y. Xu, T. Xiao, J. Zhang, K. Yang, and Z. Zhang, “Scale-invariant convolutional neural networks,” arXiv preprint arXiv:1411.6369 , 2014

Original

2014

Earlier work this paper cites.

R. Gens and P. M. Domingos, “Deep symmetry networks,” in Advances in Neural Information Processing Systems , 2014

2014

Earlier work this paper cites.

A. Giusti, J. Guzzi, D. C. Cireşan, F.-L. He, J. P. Rodríguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. Di Caro et al. , “A machine learning approach to visual perception of forest trails for mobile robots,” IEEE Robotics and Automation Letters , vol. 1, no. 2, pp. 661–667, 2015

2015

Earlier work this paper cites.

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” in Advances in Neural Information Processing Systems , 2015

2015

Earlier work this paper cites.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 2, pp. 295–307, 2015

2015

Earlier work this paper cites.

N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against deep learning systems using adversarial examples,” arXiv preprint arXiv:1602.02697 , 2016

Original

2016

Earlier work this paper cites.

G. K. Dziugaite, Z. Ghahramani, and D. M. Roy, “A study of the effect of jpg compression on adversarial images,” arXiv preprint arXiv:1608.00853 , 2016

Original

2016

Earlier work this paper cites.

S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778

Original

2016

Cited alongside, same era.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017

Original

2017

Cited alongside, same era.

C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille, “Mitigating adversarial effects through randomization,” arXiv preprint arXiv:1711.01991 , 2017

Original

2017

Cited alongside, same era.

Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman, “Pixeldefend: Leveraging generative models to understand and defend against adversarial examples,” arXiv preprint arXiv:1710.10766 , 2017

Original

2017

Cited alongside, same era.

P.-Y. Chen, Y. Sharma, H. Zhang, J. Yi, and C.-J. Hsieh, “Ead: elastic-net attacks to deep neural networks via adversarial examples,” in Thirty-second AAAI Conference on Artificial Intelligence , 2018

2018

Later among the works it cites.

C. Xie, Y. Wu, L. v. d. Maaten, A. L. Yuille, and K. He, “Feature denoising for improving adversarial robustness,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 501–509

2019

Later among the works it cites.

A. Mustafa, S. H. Khan, M. Hayat, J. Shen, and L. Shao, “Image super-resolution as a defense against adversarial attacks,” IEEE Transactions on Image Processing , vol. 29, pp. 1711–1724, 2019

2019

Later among the works it cites.

D. Zhang, T. Zhang, Y. Lu, Z. Zhu, and B. Dong, “You only propagate once: Accelerating adversarial training via maximal principle,” in Advances in Neural Information Processing Systems , 2019, pp. 227–238

2019

Later among the works it cites.