  We study black-box adversarial attacks for image classifiers in a constrained
threat model, where adversaries can only modify a small fraction of pixels in
the form of scratches on an image. We show that it is possible for adversaries
to generate localized \textit{adversarial scratches} that cover less than $5\%$
of the pixels in an image and achieve targeted success rates of $98.77\%$ and
$97.20\%$ on ImageNet and CIFAR-10 trained ResNet-50 models, respectively. We
demonstrate that our scratches are effective under diverse shapes, such as
straight lines or parabolic B\a'ezier curves, with single or multiple colors.
In an extreme condition, in which our scratches are a single color, we obtain a
targeted attack success rate of $66\%$ on CIFAR-10 with an order of magnitude
fewer queries than comparable attacks. We successfully launch our attack
against Microsoft's Cognitive Services Image Captioning API and propose various
mitigation strategies.
