Benchmarking the Robustness of Quantized Models

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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2017

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Towards deep learning models resistant to adversarial attacks

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Pact: Parameterized clipping activation for quantized neural networks

J. Choi, Z. Wang, S. Venkataramani, P. I.-J. Chuang, V. Srinivasan, and K. Gopalakrishnan · 2018

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Mobilenetv2: Inverted residuals and linear bottlenecks

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Learned step size quantization

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Benchmarking neural network robustness to common corruptions and perturbations

D. Hendrycks and T. Dietterich · 2019

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Defensive quantization: When efficiency meets robustness

J. Lin, C. Gan, and S. Han · 2019

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Perceptual-sensitive gan for generating adversarial patches

A. Liu, X. Liu, J. Fan, Y. Ma, A. Zhang, H. Xie, and D. Tao · 2019

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Gradient

M. Alizadeh, A. Behboodi, M. van Baalen, C. Louizos, T. Blankevoort, and M. Welling · 2020

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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

F. Croce and M. Hein · 2020

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Multi-dimensional pruning: A unified framework for model compression

J. Guo, W. Ouyang, and D. Xu · 2020

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Spatiotemporal attacks for embodied agents

A. Liu, T. Huang, X. Liu, Y. Xu, Y. Ma, X. Chen, S. J. Maybank, and D. Tao · 2020

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Bias-based universal adversarial patch attack for automatic check-out

A. Liu, J. Wang, X. Liu, B. Cao, C. Zhang, and H. Yu · 2020

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Training robust deep neural networks via adversarial noise propagation

A. Liu, X. Liu, H. Yu, C. Zhang, Q. Liu, and D. Tao · 2021

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Robustart: Benchmarking robustness on architecture design and training techniques

S. Tang, R. Gong, Y. Wang, A. Liu, J. Wang, X. Chen, F. Yu, X. Liu, D. Song, A. Yuille, et al · 2021

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Dual attention suppression attack: Generate adversarial camouflage in physical world

J. Wang, A. Liu, Z. Yin, S. Liu, S. Tang, and X. Liu · 2021

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Real world robustness from systematic noise

Y. Wang, Y. Li, R. Gong, T. Xiao, and F. Yu · 2021

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Harnessing perceptual adversarial patches for crowd counting

S. Liu, J. Wang, A. Liu, Y. Li, Y. Gao, X. Liu, and D. Tao · 2022

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Defensive patches for robust recognition in the physical world