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With the rapid advancement and increased use of deep learning models in image identification, security becomes a major concern to their deployment in safety-critical systems.
SAD: Saliency-based Defenses Against Adversarial Examples
Tran, R.; Patrick, D.; Geyer, M.; and Fernandez, A. 2020 · 2003
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DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
Li, Y.; Jin, W.; Xu, H.; and Tang, J. 2020 · 2005
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ImageNet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
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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 · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M. S.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
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Early methods for detecting adversarial images
Hendrycks, D.; and Gimpel, K. 2016 · 2016
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Detecting adversarial samples from artifacts
Feinman, R.; Curtin, R. R.; Shintre, S.; and Gardner, A. B. 2017 · 2017
Cited alongside, same era.
Adversarial Machine Learning at Scale
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2017 · 2017
Cited alongside, same era.
Adversarial examples detection in deep networks with convolutional filter statistics
Li, X.; and Li, F. 2017 · 2017
Cited alongside, same era.
MagNet: a Two-Pronged Defense against Adversarial Examples
Meng, D.; and Chen, H. 2017 · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W.; Evans, D.; and Qi, Y. 2017 · 2017
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Samangouei, P.; Kabkab, M.; and Chellappa, R. 2018 · 2018
Later among the works it cites.
A guide to deep learning in healthcare
Esteva, A.; Robicquet, A.; Ramsundar, B.; Kuleshov, V.; DePristo, M.; Chou, K.; Cui, C.; Corrado, G.; Thrun, S.; and Dean, J. 2019 · 2019
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High-Quality Self-Supervised Deep Image Denoising
Laine, S.; Karras, T.; Lehtinen, J.; and Aila, T. 2019 · 2019
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Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy
Lamb, A.; Verma, V.; Kannala, J.; and Bengio, Y. 2019 · 2019
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Barrage of Random Transforms for Adversarially Robust Defense
Raff, E.; Sylvester, J.; Forsyth, S.; and McLean, M. 2019 · 2019
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Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Akhtar, N.; and Mian, A. 2018 · 2018
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Athalye, A.; Carlini, N.; and Wagner, D. A. 2018 · 2018
Cited alongside, same era.
Detecting adversarial examples using data manifolds
Jha, S.; Jang, U.; Jha, S.; and Jalaian, B. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Deep learning for self-driving cars: Chances and challenges
Rao, Q.; and Frtunikj, J. 2018 · 2018
Cited alongside, same era.
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation
Cho, S.; Jun, T. J.; Oh, B.; and Kim, D. 2020 · 2020
Later among the works it cites.
A survey of deep learning-based source image forensics
Yang, P.; Baracchi, D.; Ni, R.; Zhao, Y.; Argenti, F.; and Piva, A. 2020 · 2020
Later among the works it cites.
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
Gao, R.; Liu, F.; Zhang, J.; Han, B.; Liu, T.; Niu, G.; and Sugiyama, M. 2021 · 2021
Later among the works it cites.
Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation
Li, F.; Du, X.; and Zhang, L. 2022 · 2022
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