AI Security
Expert
Adversarial ML Defense
Adversarial ML defense focuses on making machine learning models robust against inputs specifically crafted to fool them. This is especially critical for ML models used in security decisions — such as malware classifiers, fraud detectors, and network intrusion detection — where successful evasion has severe downstream consequences.
Key Capabilities
- Adversarial training and data augmentation
- Input validation and sanitization for ML inference
- Runtime detection of adversarial examples
- Certified defense methods and robustness evaluation
- Threat modeling for deployed ML systems
Tags
Adversarial ML Model Robustness Defensive Security ML Security