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Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples.
A simple weight decay can improve generalization
Krogh, A. and Hertz, J. A · 1992
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Gradient-based learning applied to document recognition
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Image reconstruction by domain-transform manifold learning
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Stable signal recovery from incomplete and inaccurate measurements
Candes, E. J., Romberg, J. K., and Tao, T · 2006
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Compressed sensing
Donoho, D. L · 2006
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Image denoising via sparse and redundant representations over learned dictionaries
Elad, M. and Aharon, M · 2006
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Bm3d image denoising with shape-adaptive principal component analysis
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2009
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User’s guide for tval3: Tv minimization by augmented lagrangian and alternating direction algorithms
Li, C., Yin, W., and Zhang, Y · 2009
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Sparse and redundant representations: from theory to applications in signal and image processing
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Image super-resolution via sparse representation
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Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization
Dong, W., Zhang, L., Shi, G., and Wu, X · 2011
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Learning sparsifying transforms
Ravishankar, S. and Bresler, Y · 2012
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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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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Structured overcomplete sparsifying transform learning with convergence guarantees and applications
Wen, B., Ravishankar, S., and Bresler, Y · 2015
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Compressed sensing using generative models
Bora, A., Jalal, A., Price, E., and Dimakis, A. G · 2017
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Robust physical-world attacks on deep learning models
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2017
Dagan: deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P. L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., et al · 2017
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On the robustness of semantic segmentation models to adversarial attacks
Arnab, A., Miksik, O., and Torr, P. H · 2018
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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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Learning a variational network for reconstruction of accelerated mri data
Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., and Knoll, F · 2018
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Learning to defense by learning to attack
Jiang, H., Chen, Z., Shi, Y., Dai, B., and Zhao, T · 2018
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Deep convolutional neural network for inverse problems in imaging
Jin, K. H., McCann, M. T., Froustey, E., and Unser, M · 2017
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When image denoising meets high-level vision tasks: A deep learning approach
Liu, D., Wen, B., Liu, X., Wang, Z., and Huang, T. S · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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One network to solve them all–solving linear inverse problems using deep projection models
Rick Chang, J., Li, C.-L., Poczos, B., Vijaya Kumar, B., and Sankaranarayanan, A. C · 2017
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Enhancenet: Single image super-resolution through automated texture synthesis
Sajjadi, M. S., Scholkopf, B., and Hirsch, M · 2017
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A deep cascade of convolutional neural networks for mr image reconstruction
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Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2017
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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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Scaling provable adversarial defenses
Wong, E., Schmidt, F., Metzen, J. H., and Kolter, J. Z · 2018
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Generating adversarial examples with adversarial networks
Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., and Song, D · 2018
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On instabilities of deep learning in image reconstruction-does ai come at a cost?
Antun, V., Renna, F., Poon, C., Adcock, B., and Hansen, A. C · 2019
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Evaluating robustness of deep image super-resolution against adversarial attacks
Choi, J.-H., Zhang, H., Kim, J.-H., Hsieh, C.-J., and Lee, J.-S · 2019
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Gan-based projector for faster recovery with convergence guarantees in linear inverse problems
Raj, A., Li, Y., and Bresler, Y · 2019
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A direct approach to robust deep learning using adversarial networks
Wang, H. and Yu, C.-N · 2019
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Wen, B., Ravishankar, S., Pfister, L., and Bresler, Y · 2019
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Dr2-net: Deep residual reconstruction network for image compressive sensing
Yao, H., Dai, F., Zhang, S., Zhang, Y., Tian, Q., and Xu, C · 2019
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