Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adaptive Quantum Scaling Model for Histogram Distribution-based Quantum Watermarking

Zheng Xing, Chan-Tong Lam, Xiaochen Yuan, Sio-Kei Im, Penousal Machado

TL;DR

The paper addresses rigidity in embedding scale for quantum watermarking by introducing the Adaptive Quantum Scaling Model (AQSM) to expand watermark data, and a Histogram Distribution-based Watermarking Mechanism (HDWM) to guide robust, distribution-aware embedding in NEQR-encoded quantum images. The proposed approach enables flexible embedding of watermarks of different sizes and improves extraction accuracy via a quantum refining step. Key contributions include AQSM for scalable watermark expansion, HDWM for histogram-guided embedding/extraction, and a robustness-focused quantum watermarking pipeline validated through MATLAB simulations with metrics such as PSNR, SSIM, and NCC. The results demonstrate invisibility and strong robustness against high-density noise and cropping, especially for larger scale factors, suggesting practical utility for flexible quantum watermarking of images.

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.

Adaptive Quantum Scaling Model for Histogram Distribution-based Quantum Watermarking

TL;DR

The paper addresses rigidity in embedding scale for quantum watermarking by introducing the Adaptive Quantum Scaling Model (AQSM) to expand watermark data, and a Histogram Distribution-based Watermarking Mechanism (HDWM) to guide robust, distribution-aware embedding in NEQR-encoded quantum images. The proposed approach enables flexible embedding of watermarks of different sizes and improves extraction accuracy via a quantum refining step. Key contributions include AQSM for scalable watermark expansion, HDWM for histogram-guided embedding/extraction, and a robustness-focused quantum watermarking pipeline validated through MATLAB simulations with metrics such as PSNR, SSIM, and NCC. The results demonstrate invisibility and strong robustness against high-density noise and cropping, especially for larger scale factors, suggesting practical utility for flexible quantum watermarking of images.

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.

Paper Structure

This paper contains 14 sections, 12 equations, 13 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. The proposed Adaptive Quantum Scaling Model (AQSM)
  4. Proposed histogram distribution-based quantum watermarking
  5. Proposed HDWM
  6. Quantum watermark embedding using HDWM
  7. Quantum watermark extraction using HDWM
  8. Experiments and analysis
  9. Capacity analysis
  10. Visual quality analysis
  11. Robustness analysis
  12. Comparisons and analysis
  13. Discussion
  14. Conclusion

Figures (13)

  • Figure 1: Overview of the Adaptive Quantum Scaling Model (AQSM). (a) Architecture of AQSM, (b) the demonstrations of $r=1$ and $r=2$.
  • Figure 2: Demonstration of proposed Quantum Block Aggregation (QBA). (a) Overview of QBA, (b) Quantum Block Splicing (QBS) transformation.
  • Figure 3: Quantum circuits of Quantum Block Splicing (QBS). (a) QBS circuit, (b) QIB circuit.
  • Figure 4: Flowchart of quantum watermark embedding.
  • Figure 5: Quantum circuit for watermark embedding.
  • ...and 8 more figures