  Adversarial perturbation of images, in which a source image is deliberately
modified with the intent of causing a classifier to misclassify the image,
provides important insight into the robustness of image classifiers. In this
work we develop two new methods for constructing adversarial perturbations,
both of which are motivated by minimizing human ability to detect changes
between the perturbed and source image. The first of these, the Edge-Aware
method, reduces the magnitude of perturbations permitted in smooth regions of
an image where changes are more easily detected. Our second method, the
Color-Aware method, performs the perturbation in a color space which accurately
captures human ability to distinguish differences in colors, thus reducing the
perceived change. The Color-Aware and Edge-Aware methods can also be
implemented simultaneously, resulting in image perturbations which account for
both human color perception and sensitivity to changes in homogeneous regions.
Because Edge-Aware and Color-Aware modifications exist for many image
perturbations techniques, we also focus on computation to demonstrate their
potential for use within more complex perturbation schemes. We empirically
demonstrate that the Color-Aware and Edge-Aware perturbations we consider
effectively cause misclassification, are less distinguishable to human
perception, and are as easy to compute as the most efficient image perturbation
techniques. Code and demo available at
https://github.com/rbassett3/Color-and-Edge-Aware-Perturbations
