  Identifying spurious correlations learned by a trained model is at the core
of refining a trained model and building a trustworthy model. We present a
simple method to identify spurious correlations that have been learned by a
model trained for image classification problems. We apply image-level
perturbations and monitor changes in certainties of predictions made using the
trained model. We demonstrate this approach using an image classification
dataset that contains images with synthetically generated spurious regions and
show that the trained model was overdependent on spurious regions. Moreover, we
remove the learned spurious correlations with an explanation based learning
approach.
