  Existing image cropping detection schemes ignore that recovering the
cropped-out contents can unveil the purpose of the behaved cropping attack.
This paper presents \textbf{CLR}-Net, a novel image protection scheme
addressing the combined challenge of image \textbf{C}ropping
\textbf{L}ocalization and \textbf{R}ecovery. We first protect the original
image by introducing imperceptible perturbations. Then, typical image
post-processing attacks are simulated to erode the protected image. On the
recipient's side, we predict the cropping mask and recover the original image.
Besides, we propose a novel \textbf{F}ine-\textbf{G}rained generative
\textbf{JPEG} simulator (FG-JPEG) as well as a feature alignment network to
improve the real-world robustness. Comprehensive experiments prove that the
quality of the recovered image and the accuracy of crop localization are both
satisfactory.
