  We propose an adversarial attack for facial class-specific Single Image
Super-Resolution (SISR) methods. Existing attacks, such as the Fast Gradient
Sign Method (FGSM) or the Projected Gradient Descent (PGD) method, are either
fast but ineffective, or effective but prohibitively slow on these networks. By
closely inspecting the surface that the MSE loss, used to train such networks,
traces under varying degradations, we were able to identify its parameterizable
property. We leverage this property to propose an adverasrial attack that is
able to locate the optimum degradation (effective) without needing multiple
gradient-ascent steps (fast). Our experiments show that the proposed method is
able to achieve a better speed vs effectiveness trade-off than the
state-of-theart adversarial attacks, such as FGSM and PGD, for the task of
unpaired facial as well as class-specific SISR.
