Adaptive Quantum Scaling Model for Histogram Distribution-based Quantum Watermarking

2025-02-25

AI Summary

This work addresses two key limitations in quantum image watermarking: inflexible embedding—particularly for variable-size watermarks—and insufficient robustness. To tackle these, we propose an Adaptive Quantum Scaling Model (AQSM) and a Histogram Distribution-Driven Watermarking Mechanism (HDWM), enabling reversible embedding and robust extraction of watermarks of arbitrary size. Furthermore, we introduce a novel quantum refinement technique that performs quantum-state-level correction of embedding errors. Unlike conventional fixed-scale approaches, our framework is the first to integrate adaptive scaling and histogram-statistics-guided strategies directly into quantum circuit design, thereby overcoming rigid scale constraints. Experimental evaluations across three image resolutions demonstrate excellent watermark imperceptibility and significantly enhanced robustness against cropping, noise, and quantum measurement attacks. Moreover, watermark extraction accuracy is markedly improved compared to baseline methods.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Adaptive Quantum Scaling Model for watermarking
• Histogram Distribution-based Watermarking Mechanism
• Quantum refining method for error correction

Innovation

• Adaptive Quantum Scaling Model
• Histogram Distribution-based Watermarking
• Quantum refining error correction