  Recent approaches employ deep learning-based solutions for the recovery of a
sharp image from its blurry observation. This paper introduces adversarial
attacks against deep learning-based image deblurring methods and evaluates the
robustness of these neural networks to untargeted and targeted attacks. We
demonstrate that imperceptible distortion can significantly degrade the
performance of state-of-the-art deblurring networks, even producing drastically
different content in the output, indicating the strong need to include
adversarially robust training not only in classification but also for image
recovery.
