Halftone Image Watermarking by Content Aware Double-sided Embedding Error Diffusion

TL;DR

This paper introduces CaDEED, a novel error diffusion-based halftone watermarking method that optimizes embedding by considering image content, noise tolerance, and perceptual importance, improving robustness and visual quality.

Contribution

The paper proposes a new content-aware double-sided embedding method, CaDEED, with variants CaDEED-EC and CaDEED-N&I, enhancing watermarking performance by incorporating content-specific factors.

Findings

CaDEED outperforms existing methods in numerical metrics.

Visual comparisons show improved watermark invisibility.

Parameter optimization confirms the method's robustness.

Abstract

In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the EDHVW methods' expected performances and limitations. Then, we propose a new general error diffusion based halftone visual watermarking (EDHVW) method, Content aware Double-sided Embedding Error Diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover images and watermark, different noise tolerance abilities of various cover image content and the different importance levels of every pixel (when being perceived) in the secret pattern (watermark). To demonstrate the effectiveness of CaDEED, we propose CaDEED with expectation constraint (CaDEED-EC) and CaDEED-NVF&IF (CaDEED-N&I). Specifically, we build CaDEED-EC by only considering the expected performances of specific cover images and watermark. By adopting the noise visibility function (NVF) and proposing the importance factor (IF) to assign weights to every embedding location and watermark pixel, respectively, we build the specific method CaDEED-N&I. In the experiments, we select the optimal parameters for NVF and IF via extensive experiments. In both the numerical and visual comparisons, the experimental results demonstrate the superiority of our proposed work.