Spread Spectrum Image Watermarking Through Latent Diffusion Model.

Area of Science:

• Computer Vision
• Digital Image Forensics
• Machine Learning

Background:

• Diffusion models generate highly realistic images, raising security concerns.
• Existing watermarking methods are vulnerable to regeneration attacks from diffusion models.
• Regeneration attacks can remove watermarks without degrading image quality.

Purpose of the Study:

• To propose a robust and traceable watermark framework resilient to diffusion model attacks.
• To ensure watermark security and imperceptibility while maintaining image quality.
• To address the limitations of traditional watermarking techniques against advanced generative models.

Main Methods:

• Developed a watermark framework based on latent diffusion models.
• Coupled spread-spectrum watermarks with diffusion noise for enhanced security.
• Utilized the diffusion model's information entropy reduction for watermark transparency.
• Employed a strength factor to control robustness and transparency.

Main Results:

• The proposed method demonstrates robustness against common and advanced attacks, including regeneration and semantic editing.
• Watermarks are securely embedded and imperceptible, verified by experimental results.
• The spread-spectrum strategy eliminates the need for a decoder, reducing training overhead.

Conclusions:

• The latent diffusion model-based watermark framework offers a secure and effective solution against sophisticated image manipulation.
• The method provides a controllable balance between watermark robustness and image transparency.
• This approach advances digital image forensics and ownership verification in the era of generative AI.