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The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations.
No-reference image quality assessment in the spatial domain
A. Mittal, A. K. Moorthy, and A. C. Bovik · 2012
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Guided image filtering
K. He, J. Sun, and X. Tang · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Video frame interpolation via adaptive convolution
S. Niklaus, L. Mai, and F. Liu · 2017
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Video frame interpolation via adaptive separable convolution
S. Niklaus, L. Mai, and F. Liu · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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Kernel-predicting convolutional networks for denoising monte carlo renderings
Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, and Fabrice Rousselle · 2017
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Deep multi-scale convolutional neural network for dynamic scene deblurring
Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee · 2017
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Motion deblurring in the wild
M. Noroozi, P. Chandramouli, and P. Favaro · 2017
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Foolbox: A python toolbox to benchmark the robustness of machine learning models, 2017
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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Learning dynamic siamese network for visual object tracking
Qing Guo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, and Song Wang · 2017
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Structure-regularized compressive tracking with online data-driven sampling
Qing Guo, Wei Feng, Ce Zhou, Chi-Man Pun, and Bin Wu · 2017
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Boosting adversarial attacks with momentum
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li · 2018
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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
ADef: an iterative algorithm to construct adversarial deformations
Rima Alaifari, Giovanni S. Alberti, and Tandri Gauksson · 2019
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Learning to synthesize motion blur
T. Brooks and J. T. Barron · 2019
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Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better
Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang · 2019
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A simple pooling-based design for real-time salient object detection
Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, and Jianmin Jiang · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L. Yuille · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L. Yuille, and Kaiming He · 2019
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Burst denoising with kernel prediction networks
B. Mildenhall, J. T. Barron, J. Chen, D. Sharlet, R. Ng, and R. Carroll · 2018
Cited alongside, same era.
Deblurgan: Blind motion deblurring using conditional adversarial networks
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas · 2018
Cited alongside, same era.
Adversarial attacks and defences competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick D. McDaniel · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
Cited alongside, same era.
Later among the works it cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Amora: Black-box adversarial morphing attack
Run Wang, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Yihao Huang, and Yang Liu · 2020
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Unrestricted adversarial examples via semantic manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David Forsyth · 2020
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Pasadena: Perceptually aware and stealthy adversarial denoise attack
Yupeng Cheng, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Shang-Wei Lin, Weisi Lin, Wei Feng, and Yang Liu · 2020
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It’s Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception
Liming Zhai, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Wei Feng, Shengchao Qin, and Yang Liu · 2020
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Adversarial Exposure Attack on Diabetic Retinopathy Imagery
Yupeng Cheng, Felix Juefei-Xu, Qing Guo, Huazhu Fu, Xiaofei Xie, Shang-Wei Lin, Weisi Lin, and Yang Liu · 2020
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Bias Field Poses a Threat to DNN-based X-Ray Recognition
Binyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, and Shengchao Qin · 2020
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Selective spatial regularization by reinforcement learned decision making for object tracking
Qing Guo, Ruize Han, Wei Feng, Zhihao Chen, and Liang Wan · 2020
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Spark: Spatial-aware online incremental attack against visual tracking
Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, and Yang Liu · 2020
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