  Recently, the vulnerability of deep image classification models to
adversarial attacks has been investigated. However, such an issue has not been
thoroughly studied for image-to-image tasks that take an input image and
generate an output image (e.g., colorization, denoising, deblurring, etc.) This
paper presents comprehensive investigations into the vulnerability of deep
image-to-image models to adversarial attacks. For five popular image-to-image
tasks, 16 deep models are analyzed from various standpoints such as output
quality degradation due to attacks, transferability of adversarial examples
across different tasks, and characteristics of perturbations. We show that
unlike image classification tasks, the performance degradation on
image-to-image tasks largely differs depending on various factors, e.g., attack
methods and task objectives. In addition, we analyze the effectiveness of
conventional defense methods used for classification models in improving the
robustness of the image-to-image models.

Computer Vision and Pattern Recognition;Image and Video Processing