The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Open accessAbstract
Abstract We show that adversarial training of supervised learning models is in fact a robust optimization procedure. To do this, we establish a general framework for increasing local stability of supervised learning models using robust optimization. The framework is general and broadly applicable to differentiable non-parametric models, e.g., Artificial Neural Networks (ANNs). Using an alternating minimization-maximization procedure, the loss of the model is minimized with respect to perturbed examples that are generated at each parameter update, rather than with respect to the original training data. Our proposed framework generalizes adversarial training, as well as previous approaches for increasing local stability of ANNs. Experimental results reveal that our approach increases the robustness of the network to existing adversarial examples, while making it harder to generate new ones. Furthermore, our algorithm improves the accuracy of the networks also on the original test data.
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Cited by
Towards Evaluating the Robustness of Neural Networks
Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859
Towards Deep Learning Models Resistant to Adversarial Attacks
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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., & Jordan, M. I. (2019) · arXiv · doi:10.48550/arxiv.1901.08573 · Cited by 1,560
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Carlini, N., & Wagner, D. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140444 · Cited by 966
Metadata
- Authors
- Shaham, U., Yamada, Y., & Negahban, S.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 307
- Pages
- 195-204
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2269778407
- Cited by
- 355
- References
- 51
- FWCI
- 29.56
- Type
- journal article
- Language
- en
- Open access
- green
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