Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations
The MLSecOps Podcast
00:00
Balancing Robustness and Security
The chapter discusses the distinction between robustness and security in adversarial attacks on ML models. It emphasizes the need to balance both aspects instead of focusing exclusively on one. The challenges of building robust models and the cost-benefit analysis of making models robust are also explored.
Transcript
Play full episode
