Erica Klarreich
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Abstract
Because it is easy to fool, machine learning must be taught how to handle adversarial inputs.
- Goodfellow, I., Shlens, J., and Szegedy, C. Explaining and Harnessing Adversarial Examples http://arxiv.org/pdf/1412.6572v3.pdf.Google Scholar
- Kantchelian, A., Tygar, J. D., and Joseph, A. Evasion and Hardening of Tree Ensemble Classifiers http://arxiv.org/pdf/1509.07892.pdf Google ScholarDigital Library
- Miyato, T., Dai, A., and Goodfellow, I. Virtual Adversarial Training for Semi-Supervised Text Classification http://arxiv.org/pdf/1605.07725v1.pdf.Google Scholar
- Papernot, N., McDaniel, P., Wu, X., Jha, X., and Swami, A. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Proceedings of the 37th IEEE Symposium on Security and Privacy, May 2016.Google Scholar
- Papernot, N., McDaniel, P., and Goodfellow, I. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples http://arxiv.org/pdf/1605.07277v1.pdf.Google Scholar
- Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. Intriguing Properties of Neural Networks https://arxiv.org/pdf/1312.6199v4.pdf.Google Scholar
Index Terms
- Learning securely
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