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Protecting Visible Watermarks Against Diffusion-Based Inpainting via Localized, Robust, and Reversible Latent
Summary
This study introduces a novel latent space perturbation method to protect visible watermarks in generative content. The approach enhances watermark resilience against removal attacks while maintaining image quality and allowing for reversible modifications.
Area of Science:
- Computer Vision
- Digital Watermarking
- Generative AI Security
Background:
- Generative content proliferation necessitates robust copyright protection and content identification methods.
- Visible watermarks are crucial for ownership attribution but are vulnerable to removal, especially through diffusion-based inpainting.
- Existing adversarial methods for watermark protection face limitations in imperceptibility, purification attack resistance, and reversibility.
Purpose of the Study:
- To develop an enhanced visible watermark protection method resilient to diffusion-based inpainting attacks.
- To address the trade-off between watermark protection effectiveness and image imperceptibility.
- To enable reversible watermark perturbation for subsequent image modifications and explore applications in generative video.
Main Methods:
- A localized perturbation method is proposed operating in the latent space of generative models.
- Reversible transformations are applied to latent vectors, using natural images as keys for enhanced resistance to purification attacks.
- The method avoids adversarial training, offering improved robustness and imperceptibility compared to pixel-space methods.
Main Results:
- The proposed method effectively protects visible watermarks against diffusion-based inpainting and purification attacks.
- It achieves a favorable balance between perturbation effectiveness and imperceptibility in both natural and generated images.
- Near-lossless recovery of original image data is possible for authorized users via key-based perturbation removal.
Conclusions:
- The latent space perturbation method offers a practical and effective solution for visible watermark protection in generative content.
- The approach demonstrates suitability for real-world applications, including generative video.
- Future work may involve further optimization and broader application in digital content security.