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Fooling a Complete Neural Network Verifier
2021
International Conference on Learning Representations
The efficient and accurate characterization of the robustness of neural networks to input perturbation is an important open problem. Many approaches exist including heuristic and exact (or complete) methods. Complete methods are expensive but their mathematical formulation guarantees that they provide exact robustness metrics. However, this guarantee is valid only if we assume that the verified network applies arbitrary-precision arithmetic and the verifier is reliable. In practice, however,
dblp:conf/iclr/ZomboriBCMJ21
fatcat:qhcu45zkhnbzzm4gdg27y2uttm