# Saliency-Aware Diffusion Reconstruction for Effective Invisible Watermark Removal

arXiv: https://arxiv.org/abs/2504.12809 · HTML: https://mlsanity.com/paper/2504.12809/

## Key facts

- Cited by 8 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 0 of its 6 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Placed in

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

## Built on by (most cited first, 8 of 8)

- [SpecGuard: Spectral Projection-based Advanced Invisible Watermarking](https://mlsanity.com/paper/2510.07302/) (2025)
- [MarkCleaner: High-Fidelity Watermark Removal via Imperceptible Micro-Geometric Perturbation](https://mlsanity.com/paper/2602.01513/) (2026)
- [Membership is Ownership: A Robust Ownership Verification Framework for Diffusion Models](https://mlsanity.com/paper/2608.28929/) (2026)
- [Disentangle and Drop: Robust Universal Removal of Image Watermarks via Reconstructive Grayscale Residual Decomposition](https://mlsanity.com/paper/2609.31693/) (2026)
- [RemTraceNet: Few-Shot Forensic Detection of Invisible Watermark Attacks](https://mlsanity.com/paper/2609.31694/) (2026)
- [Where Does the Watermark Hide? Push-Pull Disentanglement for Invisible Watermark Removal](https://mlsanity.com/paper/2609.31722/) (2026)
- [WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks](https://mlsanity.com/paper/2609.40031/) (2026)
- [Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking](https://mlsanity.com/paper/2610.02010/) (2026)

## Abstract (arXiv)

As digital content becomes increasingly ubiquitous, the need for robust watermark removal techniques has grown due to the inadequacy of existing embedding techniques, which lack robustness. This paper introduces a novel Saliency-Aware Diffusion Reconstruction (SADRE) framework for watermark elimination on the web, combining adaptive noise injection, region-specific perturbations, and advanced diffusion-based reconstruction. SADRE disrupts embedded watermarks by injecting targeted noise into latent representations guided by saliency masks although preserving essential image features. A reverse diffusion process ensures high-fidelity image restoration, leveraging adaptive noise levels determined by watermark strength. Our framework is theoretically grounded with stability guarantees and achieves robust watermark removal across diverse scenarios. Empirical evaluations on state-of-the-art (SOTA) watermarking techniques demonstrate SADRE's superiority in balancing watermark disruption and image quality. SADRE sets a new benchmark for watermark elimination, offering a flexible and reliable solution for real-world web content. Code is available on~hrefhttps://github.com/inzamamulDU/SADREhttps://github.com/inzamamulDU/SADRE.