How to beat a Bayesian adversary
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in safety-critical applications. Adversarially robu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
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| Series: | European Journal of Applied Mathematics |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0956792525000105/type/journal_article |
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