  Deep convolutional neural networks have become the gold standard for image
recognition tasks, demonstrating many current state-of-the-art results and even
achieving near-human level performance on some tasks. Despite this fact it has
been shown that their strong generalisation qualities can be fooled to
misclassify previously correctly classified natural images and give erroneous
high confidence classifications to nonsense synthetic images. In this paper we
extend that work, by presenting a straightforward way to perturb an image in
such a way as to cause it to acquire any other label from within the dataset
while leaving this perturbed image visually indistinguishable from the
original.
