Formal Verification for Neural Networks

Formal Verification in AI Safety

Using mathematical methods to prove properties of AI systems with certainty.

Verification Approaches

• Abstract Interpretation: Over-approximating neural network behavior
• SMT Solving: Encoding networks as satisfiability problems
• Interval Bound Propagation: Computing output bounds
• Certified Defenses: Provable robustness guarantees

Properties to Verify

• Adversarial robustness within epsilon-balls
• Safety constraints satisfaction
• Fairness properties
• Monotonicity and other structural properties

Challenges

• Scalability to large networks
• Handling complex architectures
• Specification of safety properties
• Computational complexity

Tools and Frameworks

• α,β-CROWN for neural network verification
• Marabou SMT-based verifier
• ERAN abstract interpretation
• TorchVerify and other libraries