Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations
The MLSecOps Podcast
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Mitigations in Robust ML and Effective Management
This chapter explores the concept of mitigations in the context of robust ML, focusing on unique proposals that don't require robust ML. It covers specific mitigations for poisoning, inversion, and evasion, including examples like data encryption and secure backups.
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