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Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time.
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
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Moped: Efficient priors for scalable variational inference in bayesian deep neural networks
Krishnan, R., Subedar, M., and Tickoo, O. (2019) · 1906
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Probabilistic modeling of deep features for out-of-distribution and adversarial detection
Ahuja, N. A., Ndiour, I., Kalyanpur, T., and Tickoo, O. (2019) · 1909
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al. (1998) · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Antidote: Understanding and defending against poisoning of anomaly detectors
Rubinstein, B. I., Nelson, B., Huang, L., Joseph, A. D., Lau, S.-h., Rao, S., Taft, N., and Tygar, J. D. (2009) · 2009
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M. (2011) · 2011
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On the practicality of integrity attacks on document-level sentiment analysis
Newell, A., Potharaju, R., Xiang, L., and Nita-Rotaru, C. (2014) · 2014
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Uncertainty in deep learning
Gal, Y. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D. (2017) · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Certified defenses for data poisoning attacks
Steinhardt, J., Koh, P. W. W., and Liang, P. S. (2017) · 2017
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Poisoning behavioral malware clustering
Biggio, B., Rieck, K., Ariu, D., Wressnegger, C., Corona, I., Giacinto, G., and Roli, F. (2018) · 2018
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Detection of adversarial training examples in poisoning attacks through anomaly detection
Paudice, A., Muñoz-González, L., György, A., and Lupu, E. C. (2018) · 2018
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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) · 2018
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Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Wen, Y., Vicol, P., Ba, J., Tran, D., and Grosse, R. (2018) · 2018
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Gu, T., Dolan-Gavitt, B., and Garg, S. (2017) · 2017
Cited alongside, same era.
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Is feature selection secure against training data poisoning?
Xiao, H., Biggio, B., Brown, G., Fumera, G., Eckert, C., and Roli, F. (2018) · 2018
Later among the works it cites.