  A growing body of work has shown that deep neural networks are susceptible to
adversarial examples. These take the form of small perturbations applied to the
model's input which lead to incorrect predictions. Unfortunately, most
literature focuses on visually imperceivable perturbations to be applied to
digital images that often are, by design, impossible to be deployed to physical
targets. We present Adversarial Scratches: a novel L0 black-box attack, which
takes the form of scratches in images, and which possesses much greater
deployability than other state-of-the-art attacks. Adversarial Scratches
leverage B\'ezier Curves to reduce the dimension of the search space and
possibly constrain the attack to a specific location. We test Adversarial
Scratches in several scenarios, including a publicly available API and images
of traffic signs. Results show that, often, our attack achieves higher fooling
rate than other deployable state-of-the-art methods, while requiring
significantly fewer queries and modifying very few pixels.
