# Blind Deep-Learning-Based Image Watermarking Robust Against Geometric Transformations

arXiv: https://arxiv.org/abs/2402.09062 · HTML: https://mlsanity.com/paper/2402.09062/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)

## Built on by (most cited first, 3 of 3)

- [Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection](https://mlsanity.com/paper/2506.02665/) (2025)
- [Exploring Adversarial Watermarking in Transformer-Based Models: Transferability and Robustness Against Defense Mechanism for Medical Images](https://mlsanity.com/paper/2506.06389/) (2025)
- [Geometry-Preserving Blind Watermarking for Raw 3D Point Clouds](https://mlsanity.com/paper/2609.32222/) (2026)

## Abstract (arXiv)

Digital watermarking enables protection against copyright infringement of images. Although existing methods embed watermarks imperceptibly and demonstrate robustness against attacks, they typically lack resilience against geometric transformations. Therefore, this paper proposes a new watermarking method that is robust against geometric attacks. The proposed method is based on the existing HiDDeN architecture that uses deep learning for watermark encoding and decoding. We add new noise layers to this architecture, namely for a differentiable JPEG estimation, rotation, rescaling, translation, shearing and mirroring. We demonstrate that our method outperforms the state of the art when it comes to geometric robustness. In conclusion, the proposed method can be used to protect images when viewed on consumers' devices.