Abstract Interpretation of ReLU Neural Networks with Optimizable Polynomial Relaxations

Abstract

Neural networks have been shown to be highly successful in a wide range of applications. However, due to their black box behavior, their applicability can be restricted in safety-critical environments and additional verification techniques are required. Many state-of-the-art verification approaches use abstract interpretation based on linear overapproximation of the activation functions. Linearly approximating non-linear activation functions clearly incurs a loss of precision. One way to overcome this limitation is the utilization of polynomial approximations. A second way shown to improve the obtained bounds is to optimize the slope of the linear relaxations. Combining these insights, we propose a method to enable similar parameter optimization for polynomial relaxations. Given arbitrary values for a polynomial’s monomial coefficients, we can obtain valid polynomial overapproximations by appropriate upward or downward shifts. Since any value of monomial coefficients can be used to obtain valid overapproximations in that way, we use gradient-based methods to optimize the choice of the monomial coefficients. Our evaluation on verifying robustness against adversarial patches on the MNIST and CIFAR10 benchmarks shows that we can verify more instances and achieve tighter bounds than state of the art bound propagation methods.