  Single-image super-resolution aims to generate a high-resolution version of a
low-resolution image, which serves as an essential component in many computer
vision applications. This paper investigates the robustness of deep
learning-based super-resolution methods against adversarial attacks, which can
significantly deteriorate the super-resolved images without noticeable
distortion in the attacked low-resolution images. It is demonstrated that
state-of-the-art deep super-resolution methods are highly vulnerable to
adversarial attacks. Different levels of robustness of different methods are
analyzed theoretically and experimentally. We also present analysis on
transferability of attacks, and feasibility of targeted attacks and universal
attacks.
