论文标题
图像保护可靠的裁剪本地化和恢复
Image Protection for Robust Cropping Localization and Recovery
论文作者
论文摘要
现有的图像裁剪检测方案忽略了恢复裁剪含量的物品可以揭示出行为造成攻击的目的。本文介绍了\ textbf {clr} -net，这是一种新的图像保护方案，涉及图像\ textbf {c} ropping \ textbf {l} ocalization和\ textbf {r} ecovery的组合挑战。我们首先通过引入不可察觉的扰动来保护原始图像。然后，模拟典型的图像后处理攻击以侵蚀受保护的图像。在收件人方面，我们预测裁剪面膜并恢复原始图像。此外，我们提出了一个新颖的\ textbf {f} ine- \ textbf {g}，使生成\ textbf {jpeg} simulator（fg-jpeg）以及一个功能对齐网络，以提高现实世界的鲁棒性。全面的实验证明，回收图像的质量和作物定位的准确性都令人满意。
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.
