MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation

• URL: http://arxiv.org/abs/2608.10166v1
• Date: Mon, 10 Aug 2026 19:33:15 GMT
• Title: MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation
• Abstract summary: We propose MarkNull, a model-agnostic watermark removal attack via on-manifold latent manipulation.<n>MarkNull is grounded in a key observation: watermarked images exhibit a strong statistical dependency between the generated latent representation and the embedded initial noise.<n>Our attacks successfully compromise Google's SynthID-Image system while preserving high visual quality and transfer effectively to video watermarking.
• Score: 17.91170892407516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital watermarking has emerged as a critical technique for provenance and copyright attribution in AI-generated imagery, yet its robustness against realistic, model-agnostic removal attacks remains poorly explored. Existing attacks either succeed only against specific generative models or achieve removal at the cost of severe visual degradation. In this paper, we propose MarkNull, a model-agnostic watermark removal attack via on-manifold latent manipulation. MarkNull is grounded in a key observation: watermarked images exhibit a strong statistical dependency between the generated latent representation and the embedded initial noise. To quantify this dependency, we introduce the Noise-Latent Alignment Score (NLAS) and formulate an optimization objective that selectively decorrelates the latent representation from the embedded watermark while preserving semantic fidelity. Extensive evaluations across different categories of watermarking paradigms, including post-hoc, fine-tuning-based, and initial-noise-based schemes, demonstrate that MarkNull reduces average bit accuracy to 53.14%, approaching random-guessing (50%), without perceptible image degradation. To further improve scalability, we propose MarkNull-A, an amortized, optimization-free variant that distills the attack into a single forward pass, achieving 0.50 s/image with modest computational overhead. Notably, our attacks successfully compromise Google's SynthID-Image system while preserving high visual quality and transfer effectively to video watermarking. Finally, we present an attack detection mechanism as a defensive counterpart to MarkNull and MarkNull-A, highlighting the necessity of developing watermark designs resilient to model-agnostic latent-space attacks.

Related papers

• RAVEN: Erasing Invisible Watermarks via Novel View Synthesis [35.417500510522835]
  In this work, we expose a fundamental vulnerability in invisible watermarks by reformulating watermark removal as a view synthesis problem.<n>Our key insight is that generating a perceptually consistent alternative view of the same semantic content, naturally removes the embedded watermark while preserving visual fidelity.<n>We introduce a zero-shot diffusion-based framework that applies controlled geometric transformations in latent space, augmented with view-guided correspondence attention to maintain structural consistency during reconstruction.
  arXiv  Detail & Related papers  (2026-01-13T18:59:58Z)
• IConMark: Robust Interpretable Concept-Based Watermark For AI Images [50.045011844765185]
  We propose IConMark, a novel in-generation robust semantic watermarking method.<n>IConMark embeds interpretable concepts into AI-generated images, making it resilient to adversarial manipulation.<n>We demonstrate its superiority in terms of detection accuracy and maintaining image quality.
  arXiv  Detail & Related papers  (2025-07-17T05:38:30Z)
• When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting [37.85082375268253]
  We study the robustness of model-specific watermarking, where watermark embedding is integrated with text-to-image generation.<n>We introduce three attack strategies: edge prediction-based, box blurring, and fine-tuning-based attacks in a no-box setting.<n>Our best-performing attack achieves a reduction in watermark detection accuracy to approximately 47.92%.
  arXiv  Detail & Related papers  (2025-07-04T15:22:20Z)
• Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models [50.73220224678009]
  Watermarking can be used to verify the origin of synthetic images generated by artificial intelligence models.<n>Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial techniques.<n>In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery.<n>Our approach significantly broadens the scope of attacks, presenting a greater challenge to the security of current watermarking techniques.
  arXiv  Detail & Related papers  (2025-06-06T12:08:02Z)
• Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.<n>We propose a diffusion model watermarking method tailored for real-world deployment.<n>Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
  arXiv  Detail & Related papers  (2025-04-21T11:18:16Z)
• Robustness of Watermarking on Text-to-Image Diffusion Models [9.277492743469235]
  We investigate the robustness of generative watermarking, which is created from the integration of watermarking embedding and text-to-image generation processing. We found that generative watermarking methods are robust to direct evasion attacks, like discriminator-based attacks, or manipulation based on the edge information in edge prediction-based attacks but vulnerable to malicious fine-tuning.
  arXiv  Detail & Related papers  (2024-08-04T13:59:09Z)
• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs [23.639074918667625]
  We propose a novel multi-bit box-free watermarking method for GANs with improved robustness against white-box attacks. The watermark is embedded by adding an extra watermarking loss term during GAN training. We show that the presence of the watermark has a negligible impact on the quality of the generated images.
  arXiv  Detail & Related papers  (2023-10-25T18:38:10Z)
• DiffWA: Diffusion Models for Watermark Attack [8.102989872457156]
  We propose DiffWA, a conditional diffusion model with distance guidance for watermark attack. The core of our method is training an image-to-image conditional diffusion model on unwatermarked images. The results show that the model can remove the watermark with good effect and make the bit error rate of watermark extraction higher than 0.4.
  arXiv  Detail & Related papers  (2023-06-22T10:45:49Z)
• Exploring Structure Consistency for Deep Model Watermarking [122.38456787761497]
  The intellectual property (IP) of Deep neural networks (DNNs) can be easily stolen'' by surrogate model attack. We propose a new watermarking methodology, namely structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed.
  arXiv  Detail & Related papers  (2021-08-05T04:27:15Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.