SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack

• URL: http://arxiv.org/abs/2607.27811v1
• Date: Thu, 30 Jul 2026 07:50:49 GMT
• Title: SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack
• Abstract summary: SPFM-Net is a semantic-prior-guided and frequency-constrained Mamba framework for invisible watermark attack.<n> SPFM-Net first employs high-ratio masking to disrupt the spatial coherence of invisible watermark signals.<n>A lightweight Mamba-based Global State-space Feature Modeling unit is introduced to separate watermark-related features from natural image content.
• Score: 13.95278003579795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing watermark attacks typically rely on predefined signal-processing operations or locally constrained restoration networks, making it difficult to capture the long-range dependencies of globally distributed watermark signals and resulting in an unfavorable trade-off between removal effectiveness and visual fidelity. In this paper, we propose SPFM-Net, a semantic-prior-guided and frequency-constrained Mamba framework for invisible watermark attack. SPFM-Net first employs high-ratio masking to disrupt the spatial coherence of invisible watermark signals, and then utilizes a partially fine-tuned pretrained Masked Autoencoder to reconstruct semantically consistent image from sparse observations while suppressing watermark-related information. A Multi-scale Residual Frequency Feature Interaction module subsequently aggregates watermark-related residual features across multiple receptive fields, while adaptively suppressing responses from watermark-irrelevant regions. To further capture the long-range dependencies of globally distributed watermark signals, a lightweight Mamba-based Global State-space Feature Modeling (GSFM) unit is introduced to separate watermark-related features from natural image content and suppress the remaining watermark traces. In addition, SPFM-Net is optimized using a multi-level objective that jointly imposes spatial-, frequency-, and edge-domain constraints, enabling effective watermark suppression while preserving perceptual quality. Extensive experiments on representative spatial-domain, transform-domain, orthogonal moment-based, and deep learning-based watermarking schemes demonstrate that SPFM-Net achieves a favorable trade-off between watermark attack effectiveness and perceptual fidelity.

Related papers

• FDDWAN: A Frequency-Decoupled Diffusion Network for Watermarking Attack [16.505828216414333]
  Existing invisible watermark removal methods often struggle to accurately capture the watermark-bearing features.<n>We propose the Frequency-Decoupled Diffusion Watermark Attack Network (FDDWAN), a coarse-to-fine framework that performs watermark removal through wavelet-domain decomposition and residual diffusion refinement.<n>Experiments on CelebA and ImageNet across four representative watermarking schemes demonstrate that FDDWAN achieves a more favorable trade-off between watermark removal effectiveness and visual fidelity than conventional and learning-based attack methods.
  arXiv  Detail & Related papers  (2026-07-30T07:38:36Z)
• Breaking Watermarks in the Frequency Domain: A Modulated Diffusion Attack Framework [25.173042326314697]
  FMDiffWA is a frequency-domain modulated diffusion framework for watermark attacks.<n>Experiments demonstrate that FMDiffWA achieves superior visual fidelity compared to existing watermark attacks.
  arXiv  Detail & Related papers  (2026-04-24T04:57:13Z)
• AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection [6.641356834127894]
  We propose a novel task named Agnostic Watermark Presence Detection (AWPD)<n>AWPD aims to identify whether an image carries a copyright mark without requiring decoding information.<n>We construct the UniFreq-100K dataset, comprising large-scale samples across various invisible watermark embedding algorithms.
  arXiv  Detail & Related papers  (2026-03-06T01:10:47Z)
• An Ensemble Framework for Unbiased Language Model Watermarking [60.99969104552168]
  We propose ENS, a novel ensemble framework that enhances the detectability and robustness of unbiased watermarks.<n>ENS sequentially composes multiple independent watermark instances, each governed by a distinct key, to amplify the watermark signal.<n> Empirical evaluations show that ENS substantially reduces the number of tokens needed for reliable detection and increases resistance to smoothing and paraphrasing attacks.
  arXiv  Detail & Related papers  (2025-09-28T19:37:44Z)
• StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models [55.05404953041403]
  We propose a novel framework that seamlessly integrates a binary watermark into the diffusion generation process.<n>We show that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.
  arXiv  Detail & Related papers  (2025-09-22T16:35:19Z)
• OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization [66.69924980864053]
  We propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process.<n> OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations.<n> Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.
  arXiv  Detail & Related papers  (2025-08-29T15:50:59Z)
• PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning [19.170393134039568]
  We present Semantic-aware Pivotal Tuning Watermarks (PT-Mark)<n>PT-Mark preserves both the semantics of diffusion images and the traceability of the watermark.<n>It achieves a 10% improvement in the performance of semantic preservation compared to state-of-the-art watermarking methods.
  arXiv  Detail & Related papers  (2025-04-15T04:25:57Z)
• Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal [57.84348166457113]
  We introduce a novel feature adapting framework that leverages the representation capacity of a pre-trained image inpainting model.<n>Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model.<n>For relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process.
  arXiv  Detail & Related papers  (2025-04-07T02:37:14Z)
• MarkPlugger: Generalizable Watermark Framework for Latent Diffusion Models without Retraining [48.41130825143742]
  In the fast-evolving era of AI-generated content (AIGC), the rapid iteration and modification of latent diffusion models (LDMs) makes retraining with watermark models costly.<n>We propose MarkPlugger, a generalizable plug-and-play watermark framework without LDM retraining.<n>Our experimental findings reveal that our method effectively harmonizes image quality and watermark recovery rate.
  arXiv  Detail & Related papers  (2024-04-08T15:29:46Z)
• WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning [68.00975867932331]
  Existing watermark removal methods mainly rely on UNet with task-specific decoder branches. We introduce an implicit joint learning paradigm to holistically integrate information from both branches. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-08-20T07:56:34Z)

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.