Definition
This field encompasses both offensive techniques to break models and defensive strategies to harden them. It involves training models on adversarial examples to improve their resilience, a process known as adversarial training. By simulating attacks during the training phase, models learn to ignore irrelevant perturbations and focus on meaningful features. This approach is crucial for ensuring reliability in high-stakes environments, balancing the trade-off between accuracy on clean data and robustness against manipulated inputs.
Summary
Adversarial machine learning is a field that studies how to make machine learning models robust against malicious inputs designed to trick them.
Key Concepts
- Adversarial Training
- Robustness
- Generalization Gap
- Security
Use Cases
- Improving model resilience
- Security auditing of AI systems
- Developing defense mechanisms