  Explanation methods shed light on the decision process of black-box
classifiers such as deep neural networks. But their usefulness can be
compromised because they are susceptible to manipulations. With this work, we
aim to enhance the resilience of explanations. We develop a unified theoretical
framework for deriving bounds on the maximal manipulability of a model. Based
on these theoretical insights, we present three different techniques to boost
robustness against manipulation: training with weight decay, smoothing
activation functions, and minimizing the Hessian of the network. Our
experimental results confirm the effectiveness of these approaches.
