  Adversarial attacks are small, carefully crafted perturbations, imperceptible
to the naked eye; that when added to an image cause deep learning models to
misclassify the image with potentially detrimental outcomes. With the rise of
artificial intelligence models in consumer safety and security intensive
industries such as self-driving cars, camera surveillance and face recognition,
there is a growing need for guarding against adversarial attacks. In this
paper, we present an approach that uses metamorphic testing principles to
automatically detect such adversarial attacks. The approach can detect image
manipulations that are so small, that they are impossible to detect by a human
through visual inspection. By applying metamorphic relations based on distance
ratio preserving affine image transformations which compare the behavior of the
original and transformed image; we show that our proposed approach can
determine whether or not the input image is adversarial with a high degree of
accuracy.
