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Backdoors and poisoning attacks are a major threat to the security of machine-learning and vision systems.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Online anomaly detection under adversarial impact
M. Kloft and P. Laskov · 2010
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Support vector machines under adversarial label noise
B. Biggio, B. Nelson, and P. Laskov · 2011
Earlier work this paper cites.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
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Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2017
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Misleading authorship attribution of source code using adversarial learning
E. Quiring, A. Maier, and K. Rieck · 2019
Later among the works it cites.
Bypassing backdoor detection algorithms in deep learning
T. J. L. Tan and R. Shokri · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
Later among the works it cites.
Seeing is not believing: Camouflage attacks on image scaling algorithms
Q. Xiao, Y. Chen, C. Shen, Y. Chen, and K. Li · 2019
Later among the works it cites.
Latent backdoor attacks on deep neural networks
Y. Yao, H. Li, H. Zheng, and B. Y. Zhao · 2019
Later among the works it cites.
Adversarial preprocessing: Understanding and preventing image-scaling attacks in machine learning
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Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2018
Cited alongside, same era.
Poison frogs! Targeted clean-label poisoning attacks on neural networks
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein · 2018
Cited alongside, same era.
E. Quiring, D. Klein, D. Arp, M. Johns, and K. Rieck · 2020
Closest in time.
The insider versus the outsider: Who poses the biggest security risk?
C. Stoneff · 2020
Closest in time.