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.
@article{arxiv.2206.02405,
title = {Image Protection for Robust Cropping Localization and Recovery},
author = {Qichao Ying and Hang Zhou and Xiaoxiao Hu and Zhenxing Qian and Sheng Li and Xinpeng Zhang},
journal= {arXiv preprint arXiv:2206.02405},
year = {2023}
}