Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks. (arXiv:2208.09872v1 [cs.LG])

The robustness of deep neural networks is crucial to modern AI-enabled
systems and should be formally verified. Sigmoid-like neural networks have been
adopted in a wide range of applications. Due to their non-linearity,
Sigmoid-like activation functions are usually over-approximated for efficient
verification, which inevitably introduces imprecision. Considerable efforts
have been devoted to finding the so-called tighter approximations to obtain
more precise verification results. However, existing tightness definitions are
heuristic and lack theoretical foundations. We conduct a thorough empirical
analysis of …

lg networks neural networks robustness verification