# Image Protection for Robust Cropping Localization and Recovery

arXiv: https://arxiv.org/abs/2206.02405 · HTML: https://mlsanity.com/paper/2206.02405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [From Image to Imuge: Immunized Image Generation](https://mlsanity.com/paper/2110.14196/) (2021)
- [RWN: Robust Watermarking Network for Image Cropping Localization](https://mlsanity.com/paper/2110.05687/) (2021)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [Dissecting Image Crops](https://mlsanity.com/paper/2011.11831/) (2020)

## Abstract (arXiv)

Existing image cropping detection schemes ignore that recovering the cropped-out contents can unveil the purpose of the behaved cropping attack. This paper presents CLR-Net, a novel image protection scheme addressing the combined challenge of image Cropping Localization and Recovery. 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 Fine-Grained generative 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.