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Current approaches to neural network verification focus on specificationsthat target small regions around known input data points, such as localrobustness. Thus, using these approaches, we can not obtain guarantees forinputs that are not close to known inputs. Yet, it is highly likely that aneural network will encounter such truly unseen inputs during its application.We study global specifications that - when satisfied - provide guarantees forall potential inputs. We introduce a hyperproperty formalism that allows forexpressing global specifications such as monotonicity, Lipschitz continuity,global robustness, and dependency fairness. Our formalism enables verifyingglobal specifications using existing neural network verification approaches byleveraging capabilities for verifying general computational graphs. Thereby, weextend the scope of guarantees that can be provided using existing methods.Recent success in verifying specific global specifications shows that attainingstrong guarantees for all potential data points is feasible.

Verifying Global Neural Network Specifications using Hyperproperties