# On the Coexistence and Ensembling of Watermarks

arXiv: https://arxiv.org/abs/2501.17356 · HTML: https://mlsanity.com/paper/2501.17356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 29 Jan 2025.

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

- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [A Review of Sparse Expert Models in Deep Learning](https://mlsanity.com/paper/2209.01667/) (2022)
- [Invisible Image Watermarks Are Provably Removable Using Generative AI](https://mlsanity.com/paper/2306.01953/) (2023)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [RoSteALS: Robust Steganography using Autoencoder Latent Space](https://mlsanity.com/paper/2304.03400/) (2023)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://mlsanity.com/paper/2404.12691/) (2024)
- [Certifying Ensembles: A General Certification Theory with S-Lipschitzness](https://mlsanity.com/paper/2304.13019/) (2023)

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

- [Learning Probabilistic Embeddings for Unsupervised Action Segmentation](https://mlsanity.com/paper/2607.05263/) (2026)
- [Watermarks Attack Watermarks: Re-Watermarking as a Generic Removal Strategy](https://mlsanity.com/paper/2605.16796/) (2026)
- [Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence](https://mlsanity.com/paper/2608.10091/) (2026)

## Abstract (arXiv)

Watermarking, the practice of embedding imperceptible information into media such as images, videos, audio, and text, is essential for intellectual property protection, content provenance and attribution. The growing complexity of digital ecosystems necessitates watermarks for different uses to be embedded in the same media. However, to detect and decode all watermarks, they need to coexist well with one another. We perform the first study of coexistence of deep image watermarking methods and, contrary to intuition, we find that various open-source watermarks can coexist with only minor impacts on image quality and decoding robustness. The coexistence of watermarks also opens the avenue for ensembling watermarking methods. We show how ensembling can increase the overall message capacity and enable new trade-offs between capacity, accuracy, robustness and image quality, without needing to retrain the base models.