New SPFM-Net framework targets invisible watermark attacks

Researchers have developed SPFM-Net, a novel framework designed for invisible watermark attacks. 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 and a fine-tuned Masked Autoencoder to reconstruct images, followed by a multi-scale residual frequency feature interaction module and a Mamba-based global state-space feature modeling unit to isolate and suppress watermark signals. The framework is optimized with multi-level constraints across spatial, frequency, and edge domains, demonstrating superior performance in watermark attack effectiveness and perceptual fidelity across various watermarking schemes. AI

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

RANK_REASON The cluster describes a new research paper detailing a novel technical approach to watermark attacks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Global State-space Feature Modeling
• Mamba
• Masked Autoencoder
• SPFM-Net

• paper
• other

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