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New SPFM-Net framework targets invisible watermarks with Mamba architecture

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SPFM-Net, a novel framework designed to attack invisible watermarks in images. This system utilizes a semantic-prior-guided and frequency-constrained Mamba architecture to effectively remove watermarks while maintaining image quality. SPFM-Net employs techniques like high-ratio masking, a fine-tuned Masked Autoencoder, and a Mamba-based Global State-space Feature Modeling unit to disrupt and suppress watermark signals. Experiments show SPFM-Net achieves a favorable balance between attack effectiveness and perceptual fidelity across various watermarking schemes. AI

IMPACT Introduces a new method for watermark attack, potentially impacting digital rights management and content authentication.

RANK_REASON The cluster describes a new research paper detailing a novel technical approach to watermark attack.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SPFM-Net framework targets invisible watermarks with Mamba architecture

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The cluster describes a new research paper detailing a novel technical approach to watermark attack.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Chunpeng Wang, Yanan Shi, Zhiqiu Xia, Jidong Yang, Suo Gao, Qi Li ·

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

    arXiv:2607.27811v1 Announce Type: new 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 a…

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