Understanding Global Robustness of Neural Networks

The author proposes a new global robustness property for classifiers to find the minimal globally robust bound, introducing VHAGaR as an anytime verifier. The approach involves encoding the problem as mixed-integer programming, pruning search space by identifying dependencies, and generalizing adversarial attacks.