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Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data.
Certified adversarial robustness via randomized smoothing
Cohen, J. M.; Rosenfeld, E.; and Kolter, J. Z. 2019 · 1902
Earlier work this paper cites.
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Gao, Y.; Xu, C.; Wang, D.; Chen, S.; Ranasinghe, D. C.; and Nepal, S. 2019 · 1902
Earlier work this paper cites.
A target-agnostic attack on deep models: Exploiting security vulnerabilities of transfer learning
Rezaei, S.; and Liu, X. 2019 · 1904
Earlier work this paper cites.
Transferable clean-label poisoning attacks on deep neural nets
Zhu, C.; Huang, W. R.; Shafahi, A.; Li, H.; Taylor, G.; Studer, C.; and Goldstein, T. 2019 · 1905
Earlier work this paper cites.
Februus: Input purification defense against trojan attacks on deep neural network systems
Doan, B. G.; Abbasnejad, E.; and Ranasinghe, D. C. 2019 · 1908
Earlier work this paper cites.
Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems
Guo, W.; Wang, L.; Xing, X.; Du, M.; and Song, D. 2019 · 1908
Earlier work this paper cites.
Hidden trigger backdoor attacks
Saha, A.; Subramanya, A.; and Pirsiavash, H. 2019 · 1910
Earlier work this paper cites.
Detecting AI Trojans Using Meta Neural Analysis
Xu, X.; Wang, Q.; Li, H.; Borisov, N.; Gunter, C. A.; and Li, B. 2019 · 1910
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TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks
Guo, C.; Wu, R.; and Weinberger, K. Q. 2020 · 2002
Earlier work this paper cites.
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Wang, B.; Cao, X.; Gong, N. Z.; et al. 2020 · 2002
Earlier work this paper cites.
Dynamic Backdoor Attacks Against Machine Learning Models
Salem, A.; Wen, R.; Backes, M.; Ma, S.; and Zhang, Y. 2020 · 2003
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Rethinking the Trigger of Backdoor Attack
Li, Y.; Zhai, T.; Wu, B.; Jiang, Y.; Li, Z.; and Xia, S. 2020b · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
Earlier work this paper cites.
Li, Y.; Wu, B.; Jiang, Y.; Li, Z.; and Xia, S.-T. 2020a · 2007
Earlier work this paper cites.
Reflection backdoor: A natural backdoor attack on deep neural networks
Liu, Y.; Ma, X.; Bailey, J.; and Lu, F. 2020a · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Stallkamp, J.; Schlipsing, M.; Salmen, J.; and Igel, C. 2012 · 2012
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Cited alongside, same era.
Deep face recognition
Parkhi, O. M.; Vedaldi, A.; Zisserman, A.; et al. 2015 · 2015
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X.; Liu, C.; Li, B.; Lu, K.; and Song, D. 2017 · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T.; Dolan-Gavitt, B.; and Garg, S. 2017 · 2017
Cited alongside, same era.
Spectral signatures in backdoor attacks
Tran, B.; Li, J.; and Madry, A. 2018 · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Tsipras, D.; Santurkar, S.; Engstrom, L.; Turner, A.; and Madry, A. 2018 · 2018
Later among the works it cites.
Clean-label backdoor attacks
Turner, A.; Tsipras, D.; and Madry, A. 2018 · 2018
Later among the works it cites.
Potrojan: powerful neural-level trojan designs in deep learning models
Zou, M.; Shi, Y.; Wang, C.; Li, F.; Song, W.; and Wang, Y. 2018 · 2018
Later among the works it cites.
DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks
Chen, H.; Fu, C.; Zhao, J.; and Koushanfar, F. 2019 · 2019
Later among the works it cites.
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Trojaning attack on neural networks
Liu, Y.; Ma, S.; Aafer, Y.; Lee, W.-C.; Zhai, J.; Wang, W.; and Zhang, X. 2017 · 2017
Cited alongside, same era.
Neural trojans
Liu, Y.; Xie, Y.; and Srivastava, A. 2017 · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M.; Fawzi, A.; Fawzi, O.; and Frossard, P. 2017 · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks
Steinhardt, J.; Koh, P. W. W.; and Liang, P. S. 2017 · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y.; Park, T.; Isola, P.; and Efros, A. A. 2017 · 2017
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A.; Sarkar, A.; Howlader, P.; and Balasubramanian, V. N. 2018 · 2018
Cited alongside, same era.
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
Later among the works it cites.
instagram-filters
instagram filters. 2019 · 2019
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FABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation
Liu, Y.; Lee, W.-C.; Tao, G.; Ma, S.; Aafer, Y.; and Zhang, X. 2019 · 2019
Later among the works it cites.
Defending neural backdoors via generative distribution modeling
Qiao, X.; Yang, Y.; and Li, H. 2019 · 2019
Later among the works it cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Wang, B.; Yao, Y.; Shan, S.; Li, H.; Viswanath, B.; Zheng, H.; and Zhao, B. Y. 2019 · 2019
Later among the works it cites.
Weather-Dataset on Kaggle (Contains 4 classes such as cloudy, rain, shine and sunrise.)
Gupta, R. 2020 · 2020
Closest in time.
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Kolouri, S.; Saha, A.; Pirsiavash, H.; and Hoffmann, H. 2020 · 2020
Closest in time.
A Survey on Neural Trojans
Liu, Y.; Mondal, A.; Chakraborty, A.; Zuzak, M.; Jacobsen, N.; Xing, D.; and Srivastava, A. 2020b · 2020
Closest in time.
TBT: Targeted Neural Network Attack with Bit Trojan
Rakin, A. S.; He, Z.; and Fan, D. 2020 · 2020
Closest in time.
An embarrassingly simple approach for trojan attack in deep neural networks
Tang, R.; Du, M.; Liu, N.; Yang, F.; and Hu, X. 2020 · 2020
Closest in time.
Latent backdoor attacks on deep neural networks
Yao, Y.; Li, H.; Zheng, H.; and Zhao, B. Y. 2019 · 2055
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