Does Symbolic Knowledge Prevent Adversarial Fooling?
arXiv:1912.10834
Abstract
Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.
Short position paper. Accepted at the Ninth International Workshop on Statistical Relational AI (StarIA 2020)