  We introduce a method for learning adversarial perturbations targeted to
individual images or videos. The learned perturbations are found to be sparse
while at the same time containing a high level of feature detail. Thus, the
extracted perturbations allow a form of object or action recognition and
provide insights into what features the studied deep neural network models
consider important when reaching their classification decisions. From an
adversarial point of view, the sparse perturbations successfully confused the
models into misclassifying, although the perturbed samples still belonged to
the same original class by visual examination. This is discussed in terms of a
prospective data augmentation scheme. The sparse yet high-quality perturbations
may also be leveraged for image or video compression.
