Generic Reversible Visible Watermarking Via Regularized Graph Fourier Transform Coding

TL;DR

This paper introduces a versatile reversible visible watermarking framework using regularized Graph Fourier Transform coding, enabling efficient compression and embedding of difference images for high-quality, reversible watermarking across various schemes.

Contribution

A novel generic RVW framework employing GFT coding for efficient difference image compression, enhancing versatility and visual quality over existing methods.

Findings

Outperforms state-of-the-art methods in versatility.

Achieves higher visual quality of watermarked images.

Provides effective reversible recovery of original images.

Abstract

Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images or video frames to declare the copyright ownership information, but also completely erases the visible watermark image and thus enables restoring the original host image without any distortion. However, existing RVW algorithms mostly construct the reversible mapping mechanism for a specific visible watermarking scheme, which is not versatile. Hence, we propose a generic RVW framework to accommodate various visible watermarking schemes. In particular, we obtain a reconstruction data packet -- the compressed difference image between the watermarked image and the original host image, which is embedded into the watermarked image via any conventional reversible data hiding method to facilitate the blind recovery of the host image. The key is to achieve compact compression of the difference image for efficient embedding of the reconstruction data packet. To this end, we propose regularized Graph Fourier Transform (GFT) coding, where the difference image is smoothed via the graph Laplacian regularizer for more efficient compression and then encoded by multi-resolution GFTs in an approximately optimal manner. Experimental results show that the proposed framework has much better versatility than state-of-the-art methods. Due to the small amount of auxiliary information to be embedded, the visual quality of the watermarked image is also higher.