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.
Categories: eess.IV cs.CV