In this paper, we introduce BNN-DP, an efficient algorithmic framework for
analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a
compact set of input points $T\subset \mathbb{R}^n$, BNN-DP computes lower and
upper bounds on the BNN's predictions for all the points in $T$. The framework
is based on an interpretation of BNNs as stochastic dynamical systems, which
enables the use of Dynamic Programming (DP) algorithms to bound the prediction
range along the layers of the network. Specifically, the method uses bound
propagation techniques and convex relaxations to derive a backward recursion
procedure to over-approximate the prediction range of the BNN with piecewise
affine functions. The algorithm is general and can handle both regression and
classification tasks. On a set of experiments on various regression and
classification tasks and BNN architectures, we show that BNN-DP outperforms
state-of-the-art methods by up to four orders of magnitude in both tightness of
the bounds and computational efficiency.

本文介绍了 BNN-DP，它是一种用于分析贝叶斯神经网络的对抗鲁棒性的高效算法框架。该算法使用动态规划算法来限定网络的预测范围，同时具备一般性和高效性。