Machine Learning Security: Adversarial Attacks and Model Poisoning

Introduction

Machine Learning systems face unique security challenges as AI becomes integrated into critical infrastructure. This analysis examines adversarial attacks, model poisoning, and defensive strategies for ML security.

Adversarial Attack Taxonomy

Evasion Attacks

• Gradient-based attacks using FGSM and PGD
• Decision boundary analysis for minimal perturbations
• Transferability exploits across different models
• Black-box attacks using query-based optimization

Poisoning Attacks

• Training data contamination for backdoor insertion
• Label flipping for classification manipulation
• Feature selection attacks targeting specific inputs
• Federated learning poisoning in distributed systems

Deep Learning Vulnerabilities

Neural Network Exploits

• Adversarial examples in computer vision systems
• Semantic attacks preserving visual similarity
• Physical world attacks using printed adversarial patches
• Audio adversarial examples for speech recognition systems

Model Extraction Attacks

• Functionality extraction through query analysis
• Architecture reconstruction via side-channel analysis
• Parameter estimation using optimization techniques
• Watermark removal for intellectual property theft

Case Study: Autonomous Vehicle Security

LiDAR Spoofing

• Point cloud manipulation for object misclassification
• Sensor fusion attacks targeting multiple modalities
• Physical adversarial objects for real-world exploitation
• Safety system bypass through ML model deception

Defensive Strategies

Adversarial Training

• Robust optimization with adversarial examples
• Certified defenses with provable guarantees
• Ensemble methods for attack resilience
• Detection mechanisms for adversarial inputs

Privacy-Preserving ML

• Differential privacy for training data protection
• Federated learning security enhancements
• Homomorphic encryption for computation privacy
• Secure multi-party computation protocols

Conclusion

ML security requires understanding both traditional cybersecurity and novel AI-specific threats, demanding comprehensive defensive strategies.