Adaptive Quantum Scaling Model for Histogram Distribution-based Quantum Watermarking

TL;DR

This paper introduces an adaptive quantum watermarking scheme that uses histogram distribution properties and quantum refining to enhance robustness and flexibility in quantum image watermarking.

Contribution

It proposes a novel adaptive quantum scaling model and a histogram-based watermarking mechanism, improving flexibility and robustness over existing fixed-scale methods.

Findings

Demonstrates high invisibility of watermarks in simulations.

Shows improved robustness against attacks.

Achieves accurate watermark extraction with quantum error correction.

Abstract

The development of quantum image representation and quantum measurement techniques has made quantum image processing research a hot topic. In this paper, a novel Adaptive Quantum Scaling Model (AQSM) is first proposed for scrambling watermark images. Then, on the basis of the proposed AQSM, a novel quantum watermarking scheme is presented. Unlike existing quantum watermarking schemes with fixed embedding scales, the proposed method can flexibly embed watermarks of different sizes. In order to improve the robustness of the watermarking algorithm, a novel Histogram Distribution-based Watermarking Mechanism (HDWM) is proposed, which utilizes the histogram distribution property of the watermark image to determine the embedding strategy. In order to improve the accuracy of extracted watermark information, a quantum refining method is suggested, which can realize a certain error correction. The required key quantum circuits are designed. Finally, the effectiveness and robustness of the proposed quantum watermarking method are evaluated by simulation experiments on three image size scales. The results demonstrate the invisibility and good robustness of the watermarking algorithm.