Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations

Speakers discuss concerns of customers and clients regarding security of AI applications and machine learning systems. They explore the distinction between robustness and security in adversarial attacks on ML models. The concept of mitigations in robust ML, including data encryption and secure backups, is discussed. The use of cryptographic signature for data and supply chain validation for data poisoning protection are examined. Techniques of model inversion and differential privacy in adversarial ML are explained. Building effective machine learning models with clear goals is emphasized.