Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs

Understand

Neural networks are commonly used in safety-critical real-world applications.

• Unfortunately, the predicted output is often highly sensitive to small, and possibly imperceptible, changes to the input data.
• Proving that either no such adversarial examples exist, or providing a concrete instance, is therefore crucial to ensure safe applications.
• As enumerating and testing all potential adversarial examples is computationally infeasible, verification techniques have been developed to provide mathematically sound proofs of their absence using overestimations of the network activations.