| Autor: |
Hartnett, Gavin S., Lohn, Andrew J., Sedlack, Alexander P. |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers. We use current state-of-the-art adversarially-resistant neural network classifiers [1] as the underlying models. Cost-sensitive predictions are then achieved via a final processing step in the feed-forward evaluation of the network. We evaluate the effectiveness of cost-sensitive classifiers against a variety of attacks and we introduce a new cost-sensitive attack which performs better than targeted attacks in some cases. We also explored the measures a defender can take in order to limit their vulnerability to these attacks. This attacker/defender scenario is naturally framed as a two-player zero-sum finite game which we analyze using game theory. |
| Databáze: |
arXiv |
| Externí odkaz: |
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