  Deep neural networks are the default choice of learning models for computer
vision tasks. Extensive work has been carried out in recent years on explaining
deep models for vision tasks such as classification. However, recent work has
shown that it is possible for these models to produce substantially different
attribution maps even when two very similar images are given to the network,
raising serious questions about trustworthiness. To address this issue, we
propose a robust attribution training strategy to improve attributional
robustness of deep neural networks. Our method carefully analyzes the
requirements for attributional robustness and introduces two new regularizers
that preserve a model's attribution map during attacks. Our method surpasses
state-of-the-art attributional robustness methods by a margin of approximately
3% to 9% in terms of attribution robustness measures on several datasets
including MNIST, FMNIST, Flower and GTSRB.
