Fetching the paper…
Reading the bibliography…
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
Adversarial machine learning–industry perspectives
Kumar, R. S. S., Nyström, M., Lambert, J., Marshall, A., Goertzel, M., Comissoneru, A., Swann, M., and Xia, S · 2002
Earlier work this paper cites.
Dynamic backdoor attacks against machine learning models
Salem, A., Wen, R., Backes, M., Ma, S., and Zhang, Y · 2003
Earlier work this paper cites.
Bullseye polytope: A scalable clean-label poisoning attack with improved transferability
Aghakhani, H., Meng, D., Wang, Y.-X., Kruegel, C., and Vigna, G · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Venomave: Clean-label poisoning against speech recognition
Aghakhani, H., Eisenhofer, T., Schönherr, L., Kolossa, D., Holz, T., Kruegel, C., and Vigna, G · 2010
Earlier work this paper cites.
Don’t trigger me! a triggerless backdoor attack against deep neural networks
Salem, A., Backes, M., and Zhang, Y · 2010
Earlier work this paper cites.
Poisoning attacks against support vector machines
Biggio, B., Nelson, B., and Laskov, P · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
Earlier work this paper cites.
Support vector machines under adversarial label contamination
Xiao, H., Biggio, B., Nelson, B., Xiao, H., Eckert, C., and Roli, F · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Data poisoning attacks on factorization-based collaborative filtering
Li, B., Wang, Y., Singh, A., and Vorobeychik, Y · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
Earlier work this paper cites.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
Cited alongside, same era.
Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X · 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
Cited alongside, same era.
Towards poisoning of deep learning algorithms with back-gradient optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F · 2017
Cited alongside, same era.
Targeted poisoning attacks on social recommender systems
Hu, R., Guo, Y., Pan, M., and Gong, Y · 2019
Later among the works it cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J., Li, J., and McAuley, J · 2019
Later among the works it cites.
Hidden trigger backdoor attacks
Saha, A., Subramanya, A., and Pirsiavash, H · 2019
Later among the works it cites.
Latent backdoor attacks on deep neural networks
Yao, Y., Li, H., Zheng, H., and Zhao, B. Y · 2019
Later among the works it cites.
Transferable clean-label poisoning attacks on deep neural nets
Zhu, C., Huang, W. R., Li, H., Taylor, G., Studer, C., and Goldstein, T · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The marginal value of adaptive gradient methods in machine learning
Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., and Recht, B · 2017
Cited alongside, same era.
Poisoning attacks to graph-based recommender systems
Fang, M., Yang, G., Gong, N. Z., and Liu, J · 2018
Cited alongside, same era.
Stronger data poisoning attacks break data sanitization defenses
Koh, P. W., Steinhardt, J., and Liang, P · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Liu, K., Dolan-Gavitt, B., and Garg, S · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Shafahi, A., Huang, W. R., Najibi, M., Suciu, O., Studer, C., Dumitras, T., and Goldstein, T · 2018
Cited alongside, same era.
Clean-label backdoor attacks
Turner, A., Tsipras, D., and Madry, A · 2018
Cited alongside, same era.
Dong, Y., Fu, Q.-A., Yang, X., Pang, T., Su, H., Xiao, Z., and Zhu, J · 2020
Closest in time.
Influence function based data poisoning attacks to top-n recommender systems
Fang, M., Gong, N. Z., and Liu, J · 2020
Closest in time.
Witches’ brew: Industrial scale data poisoning via gradient matching
Geiping, J., Fowl, L., Huang, W. R., Czaja, W., Taylor, G., Moeller, M., and Goldstein, T · 2020
Closest in time.
Metapoison: Practical general-purpose clean-label data poisoning
Huang, W. R., Geiping, J., Fowl, L., Taylor, G., and Goldstein, T · 2020
Closest in time.
Adversarial machine learning-industry perspectives
Kumar, R. S. S., Nyström, M., Lambert, J., Marshall, A., Goertzel, M., Comissoneru, A., Swann, M., and Xia, S · 2020
Closest in time.
The open images dataset v4
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al · 2020
Closest in time.
Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks
Liu, S., Lu, S., Chen, X., Feng, Y., Xu, K., Al-Dujaili, A., Hong, M., and O’Reilly, U.-M · 2020
Closest in time.
Deep k-nn defense against clean-label data poisoning attacks
Peri, N., Gupta, N., Huang, W. R., Fowl, L., Zhu, C., Feizi, S., Goldstein, T., and Dickerson, J. P · 2020
Closest in time.
Poisoning attacks on algorithmic fairness
Solans, D., Biggio, B., and Castillo, C · 2020
Closest in time.