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Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Backdoor learning: A survey, 2020
Y. Li, B. Wu, Y. Jiang, Z. Li, and S.-T. Xia · 2004
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An analysis of single layer networks in unsupervised feature learning
A. Coates, H. Lee, and A. Y. Ng · 2011
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2012
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Deep learning based imaging data completion for improved brain disease diagnosis
R. Li, W. Zhang, H.-I. Suk, L. Wang, J. Li, D. Shen, and S. Ji · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Adam: A method for stochastic optimization
J. Ba D. P. Kingma · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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DeepFool: a simple and accurate method to fool deep neural networks
S.-M. M.-Dezfooli, A. Fawzi, and P. Frossard · 2016
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
B. Chen, W. Carvalho, N. Baracaldo, H. Ludwig, B. Edwards, T. Lee, I. Molloy, and B. Srivastava · 2018
Cited alongside, same era.
Sentinet: Detecting physical attacks against deep learning systems, 2018
E. Chou, F. Tramèr, G. Pellegrino, and D. Boneh · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
Cited alongside, same era.
Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Madry · 2018
Cited alongside, same era.
Robust anomaly detection and backdoor attack detection via differential privacy
M. Du, R. Jia, and D. Song · 2020
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Universal litmus patterns: Revealing backdoor attacks in cnns
S. Kolouri, A. Saha, H. Pirsiavash, and H. Hoffmann · 2020
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Adversarial learning in statistical classification: A comprehensive review of defenses against attacks
D. J. Miller, Z. Xiang, and G. Kesidis · 2020
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Practical detection of trojan neural networks: Data-limited and data-free cases
R. Wang, G. Zhang, S. Liu, P.-Y. Chen, J. Xiong, and M. Wang · 2020
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Detection of backdoors in trained classifiers without access to the training set
Z. Xiang, D. J. Miller, and G. Kesidis · 2020
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Adversarial attacks and defenses in images, graphs and text: A review
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Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
H. Chen, C. Fu, J. Zhao, and F. Koushanfar · 2019
Cited alongside, same era.
Badnets: Evaluating backdooring attacks on deep neural networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
Cited alongside, same era.
TABOR: A highly accurate approach to inspecting and restoring Trojan backdoors in AI systems
W. Guo, L. Wang, X. Xing, M. Du, and D. Song · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, and X. Zhang · 2019
Cited alongside, same era.
Clean-label backdoor attacks
A. Turner, D. Tsipras, and A. Madry · 2019
Cited alongside, same era.
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
Cited alongside, same era.
A benchmark study of backdoor data poisoning defenses for deep neural network classifiers and a novel defense
Z. Xiang, D.J. Miller, and G. Kesidis · 2019
Cited alongside, same era.
H. Xu, Y. Ma, H.-C. Liu, D. Deb, H. Liu, J.-L. Tang, and A. K. Jain · 2020
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Backdoor embedding in convolutional neural network models via invisible perturbation
H. Zhong, C. Liao, A. Squicciarini, S. Zhu, and D.J. Miller · 2020
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Black-box detection of backdoor attacks with limited information and data
Y. Dong, X. Yang, Z. Deng, T. Pang, Z. Xiao, H. Su, and J. Zhu · 2021
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Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Y. Li, X. Lyu, N. Koren, L. Lyu, B. Li, and X. Ma · 2021
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Detecting AI Trojans Using Meta Neural Analysis
X. Xu, Q. Wang, H. Li, N. Borisov, C. A. Gunter, and B. Li · 2021
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Backdoor attack against speaker verification
T. Zhai, Y. Li, Z. Zhang, B. Wu, Y. Jiang, and S.-T. Xia · 2021
Later among the works it cites.
Backdoor defense via decoupling the training process
K. Huang, Y. Li, B. Wu, Z. Qin, and K. Ren · 2022
Closest in time.
Few-shot backdoor attacks on visual object tracking
Y. Li, H. Zhong, X. Ma, Y. Jiang, and S.-T. Xia · 2022
Closest in time.