# Text-Guided Image Invariant Feature Learning for Robust Image Watermarking

arXiv: https://arxiv.org/abs/2503.13805 · HTML: https://mlsanity.com/paper/2503.13805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Optimizing Neural Networks with Kronecker-factored Approximate Curvature](https://mlsanity.com/paper/1503.05671/) (2015)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations](https://mlsanity.com/paper/2202.10638/) (2022)
- [Empowering Graph Invariance Learning with Deep Spurious Infomax](https://mlsanity.com/paper/2407.11083/) (2024)
- [Deep Learning-based Text-in-Image Watermarking](https://mlsanity.com/paper/2404.13134/) (2024)

## Built on by (most cited first, 1 of 1)

- [TIACam: Text-Anchored Invariant Feature Learning with Auto-Augmentation for Camera-Robust Zero-Watermarking](https://mlsanity.com/paper/2602.18863/) (2026)

## Abstract (arXiv)

Ensuring robustness in image watermarking is crucial for and maintaining content integrity under diverse transformations. Recent self-supervised learning (SSL) approaches, such as DINO, have been leveraged for watermarking but primarily focus on general feature representation rather than explicitly learning invariant features. In this work, we propose a novel text-guided invariant feature learning framework for robust image watermarking. Our approach leverages CLIP's multimodal capabilities, using text embeddings as stable semantic anchors to enforce feature invariance under distortions. We evaluate the proposed method across multiple datasets, demonstrating superior robustness against various image transformations. Compared to state-of-the-art SSL methods, our model achieves higher cosine similarity in feature consistency tests and outperforms existing watermarking schemes in extraction accuracy under severe distortions. These results highlight the efficacy of our method in learning invariant representations tailored for robust deep learning-based watermarking.