Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks

Understand

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.