# RWN: Robust Watermarking Network for Image Cropping Localization

arXiv: https://arxiv.org/abs/2110.05687 · HTML: https://mlsanity.com/paper/2110.05687/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [A Robust Blind Watermarking Using Convolutional Neural Network](https://mlsanity.com/paper/1704.03248/) (2017)
- [Markpainting: Adversarial Machine Learning meets Inpainting](https://mlsanity.com/paper/2106.00660/) (2021)
- [Dissecting Image Crops](https://mlsanity.com/paper/2011.11831/) (2020)

## Built on by (most cited first, 9 of 9)

- [FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models](https://mlsanity.com/paper/2410.02761/) (2024)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking](https://mlsanity.com/paper/2412.01615/) (2024)
- [V2A-Mark: Versatile Deep Visual-Audio Watermarking for Manipulation Localization and Copyright Protection](https://mlsanity.com/paper/2404.16824/) (2024)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields](https://mlsanity.com/paper/2502.19125/) (2025)
- [RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces](https://mlsanity.com/paper/2602.20618/) (2026)
- [Creating Blank Canvas Against AI-enabled Image Forgery](https://mlsanity.com/paper/2511.22237/) (2025)
- [Image Protection for Robust Cropping Localization and Recovery](https://mlsanity.com/paper/2206.02405/) (2022)

## Abstract (arXiv)

Image cropping can be maliciously used to manipulate the layout of an image and alter the underlying meaning. Previous image crop detection schemes only predicts whether an image has been cropped, ignoring which part of the image is cropped. This paper presents a novel robust watermarking network (RWN) for image crop localization. We train an anti-crop processor (ACP) that embeds a watermark into a target image. The visually indistinguishable protected image is then posted on the social network instead of the original image. At the recipient's side, ACP extracts the watermark from the attacked image, and we conduct feature matching on the original and extracted watermark to locate the position of the crop in the original image plane. We further extend our scheme to detect tampering attack on the attacked image. Besides, we explore a simple yet efficient method (JPEG-Mixup) to improve the generalization of JPEG robustness. According to our comprehensive experiments, RWN is the first to provide high-accuracy and robust image crop localization. Besides, the accuracy of tamper detection is comparable with many state-of-the-art passive-based methods.