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As machine learning systems become more pervasive in safety-critical tasks, it is important to carefully analyze their robustness against attack. Our work focuses on developing an extensible framework for verifying adversarial robustness in machine learning systems over time, leveraging existing methods from probabilistic model checking and optimization. We present preliminary progress and consider future directions for verifying several key properties against sophisticated, dynamic attackers.
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Adversarial Robustness of AI Agents Acting in Probabilistic Environments
As machine learning systems become more pervasive in safety-critical tasks, it is important to carefully analyze their robustness against attack. Our work focuses on developing an extensible framework for verifying adversarial robustness in machine learning systems over time, leveraging existing methods from probabilistic model checking and optimization. We present preliminary progress and consider future directions for verifying several key properties against sophisticated, dynamic attackers.
more »
« less
- Award ID(s):
- 1801546
- PAR ID:
- 10168335
- Date Published:
- Journal Name:
- Workshop on Foundations of Computer Security (FCS) 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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