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Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Mcdnn: An approximation-based execution framework for deep stream processing under resource constraints
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database
C.-Y. Hung, W.-C. Chen, P.-T. Lai, C.-H. Lin, and C.-C. Lee · 2017
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Towards robust neural networks via random self-ensemble
X. Liu, M. Cheng, H. Zhang, and C.-J. Hsieh · 2017
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Variational dropout sparsifies deep neural networks
D. Molchanov, A. Ashukha, and D. Vetrov · 2017
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Spectral norm regularization for improving the generalizability of deep learning
Y. Yoshida and T. Miyato · 2017
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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A. S. Rakin, J. Yi, B. Gong, and D. Fan · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Structured pruning for efficient convnets via incremental regularization
H. Wang, Q. Zhang, Y. Wang, and H. Hu · 2018
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Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2018
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Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Stochastic activation pruning for robust adversarial defense
G. S. Dhillon, K. Azizzadenesheli, J. D. Bernstein, J. Kossaifi, A. Khanna, Z. C. Lipton, and A. Anandkumar · 2018
Cited alongside, same era.
Sparse dnns with improved adversarial robustness
Y. Guo, C. Zhang, C. Zhang, and Y. Chen · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Show-and-fool: Crafting adversarial examples for neural image captioning
H. Chen, H. Zhang, P.-Y. Chen, J. Yi, and C.-J. Hsieh
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh
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Defending dnn adversarial attacks with pruning and logits augmentation, 2018
S. Ye, S. Wang, X. Wang, B. Yuan, W. Wen, and X. Lin · 2018
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Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack
Z. He and D. Fan · 2019
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Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack
Z. He, A. S. Rakin, and D. Fan · 2019
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Defensive quantization: When efficiency meets robustness
J. Lin, C. Gan, and S. Han · 2019
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Second rethinking of network pruning in the adversarial setting
S. Ye, K. Xu, S. Liu, H. Cheng, J.-H. Lambrechts, H. Zhang, A. Zhou, K. Ma, Y. Wang, and X. Lin · 2019
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