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Backdoor attacks have been considered a severe security threat to deep learning.
Color and spatial structure in natural scenes
G. J. Burton and I. R. Moorhead · 1987
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D. Tolhurst, Y. Tadmor, and T. Chao · 1992
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The german traffic sign recognition benchmark: a multi-class classification competition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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Targeted backdoor attacks on deep learning systems using data poisoning, 2017
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Neural trojans
Y. Liu, Y. Xie, and A. Srivastava · 2017
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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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
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Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2018
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Detection of adversarial training examples in poisoning attacks through anomaly detection, 2018
A. Paudice, L. Muñoz-González, A. Gyorgy, and E. C. Lupu · 2018
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Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Madry · 2018
Cited alongside, same era.
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.
Robust anomaly detection and backdoor attack detection via differential privacy
M. Du, R. Jia, and D. Song · 2019
Cited alongside, same era.
Strip: A defence against trojan attacks on deep neural networks
Y. Gao, C. Xu, D. Wang, S. Chen, D. C. Ranasinghe, and S. Nepal · 2019
Cited alongside, same era.
Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems, 2019
W. Guo, L. Wang, X. Xing, M. Du, and D. Song · 2019
Cited alongside, same era.
Smoothfool: An efficient framework for computing smooth adversarial perturbations
A. Dabouei, S. Soleymani, F. Taherkhani, J. Dawson, and N. Nasrabadi · 2020
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Leveraging frequency analysis for deep fake image recognition
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz · 2020
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Intrinsic certified robustness of bagging against data poisoning attacks, 2020
J. Jia, X. Cao, and N. Z. Gong · 2020
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Deep partition aggregation: Provable defense against general poisoning attacks, 2020
A. Levine and S. Feizi · 2020
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Deep k-nn defense against clean-label data poisoning attacks, 2020
N. Peri, N. Gupta, W. R. Huang, L. Fowl, C. Zhu, S. Feizi, T. Goldstein, and J. P. Dickerson · 2020
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Hidden trigger backdoor attacks
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S. Li, B. Z. H. Zhao, J. Yu, M. Xue, D. Kaafar, and H. Zhu · 2019
Cited alongside, same era.
Nic: Detecting adversarial samples with neural network invariant checking
S. Ma, Y. Liu, G. Tao, W. Lee, and X. Zhang · 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. Zhao · 2019
Cited alongside, same era.
Detecting ai trojans using meta neural analysis
X. Xu, Q. Wang, H. Li, N. Borisov, C. A. Gunter, and B. Li · 2019
Cited alongside, same era.
Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff, 2020
E. Borgnia, V. Cherepanova, L. Fowl, A. Ghiasi, J. Geiping, M. Goldblum, T. Goldstein, and A. Gupta · 2020
Cited alongside, same era.
Sentinet: Detecting localized universal attacks against deep learning systems
E. Chou, F. Tramèr, and G. Pellegrino · 2020
Cited alongside, same era.
A. Saha, A. Subramanya, and H. Pirsiavash · 2020
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Facehack: Triggering backdoored facial recognition systems using facial characteristics
E. Sarkar, H. Benkraouda, and M. Maniatakos · 2020
Later among the works it cites.
Backdoor attacks on facial recognition in the physical world
E. Wenger, J. Passananti, Y. Yao, H. Zheng, and B. Y. Zhao · 2020
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Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation
Y. Zeng, H. Qiu, S. Guo, T. Zhang, M. Qiu, and B. Thuraisingham · 2020
Later among the works it cites.
Certified robustness of nearest neighbors against data poisoning attacks, 2021
J. Jia, X. Cao, and N. Z. Gong · 2021
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