  With an ever-increasing reliance on machine learning (ML) models in the real
world, adversarial examples threaten the safety of AI-based systems such as
autonomous vehicles. In the image domain, they represent maliciously perturbed
data points that look benign to humans (i.e., the image modification is not
noticeable) but greatly mislead state-of-the-art ML models. Previously,
researchers ensured the imperceptibility of their altered data points by
restricting perturbations via $\ell_p$ norms. However, recent publications
claim that creating natural-looking adversarial examples without such
restrictions is also possible. With much more freedom to instill malicious
information into data, these unrestricted adversarial examples can potentially
overcome traditional defense strategies as they are not constrained by the
limitations or patterns these defenses typically recognize and mitigate. This
allows attackers to operate outside of expected threat models. However,
surveying existing image-based methods, we noticed a need for more human
evaluations of the proposed image modifications. Based on existing
human-assessment frameworks for image generation quality, we propose SCOOTER -
an evaluation framework for unrestricted image-based attacks. It provides
researchers with guidelines for conducting statistically significant human
experiments, standardized questions, and a ready-to-use implementation. We
propose a framework that allows researchers to analyze how imperceptible their
unrestricted attacks truly are.
