The protection of multimedia information against copying and tampering attacks by unauthorized parties is a challenge nowadays because of the tremendous increase in digital communications. Based on this, this paper presents a quantum blind image watermarking scheme incorporating Improved Novel Enhanced Representation (INEQR) for efficient image encoding, two-dimensional Quantum Discrete Cosine Transform (QDCT) frequency domain processing, and adaptive QIM for watermark embedding. The selection of watermarking parameters in this framework is carried out in a completely secure manner using Quantum Key Distribution (QKD) based on BB84. Simulation work on 30 standard grayscale images shows the strong imperceptibility, achieving PSNR values in the range of 52–58 dB and SSIM values between 0.98 and 0.999. and a more than 95 percent accuracy level in watermark recovery under attacks such as noise, compression attacks, and geometric attacks, thus authenticating PSNR-52– 58 dB and SSIM-0.98–0.999. Robustness analysis performed against signal processing and geometric attacks indicates reliable extraction of watermarks, where the proposed framework attains an extraction accuracy of more than 95% under additive noise and filtering scenarios and moderate robustness against geometric attacks such as rotation and scaling. These indicate that the proposed framework is effective and provides imperceptible blind image watermarks based on the quantum approach.
The protection of multimedia information against copying and tampering attacks by unauthorized parties is a challenge nowadays because of the tremendous increase in digital communications. Based on this, this paper presents a quantum blind image watermarking scheme incorporating Improved Novel Enhanced Representation (INEQR) for efficient image encoding, two-dimensional Quantum Discrete Cosine Transform (QDCT) frequency domain processing, and adaptive QIM for watermark embedding. The selection of watermarking parameters in this framework is carried out in a completely secure manner using Quantum Key Distribution (QKD) based on BB84. Simulation work on 30 standard grayscale images shows the strong imperceptibility, achieving PSNR values in the range of 52–58 dB and SSIM values between 0.98 and 0.999. and a more than 95 percent accuracy level in watermark recovery under attacks such as noise, compression attacks, and geometric attacks, thus authenticating PSNR-52– 58 dB and SSIM-0.98–0.999. Robustness analysis performed against signal processing and geometric attacks indicates reliable extraction of watermarks, where the proposed framework attains an extraction accuracy of more than 95% under additive noise and filtering scenarios and moderate robustness against geometric attacks such as rotation and scaling. These indicate that the proposed framework is effective and provides imperceptible blind image watermarks based on the quantum approach.
The protection of multimedia information against copying and tampering attacks by unauthorized parties is a challenge nowadays because of the tremendous increase in digital communications. Based on this, this paper presents a quantum blind image watermarking scheme incorporating Improved Novel Enhanced Representation (INEQR) for efficient image encoding, two-dimensional Quantum Discrete Cosine Transform (QDCT) frequency domain processing, and adaptive QIM for watermark embedding. The selection of watermarking parameters in this framework is carried out in a completely secure manner using Quantum Key Distribution (QKD) based on BB84. Simulation work on 30 standard grayscale images shows the strong imperceptibility, achieving PSNR values in the range of 52–58 dB and SSIM values between 0.98 and 0.999. and a more than 95 percent accuracy level in watermark recovery under attacks such as noise, compression attacks, and geometric attacks, thus authenticating PSNR-52– 58 dB and SSIM-0.98–0.999. Robustness analysis performed against signal processing and geometric attacks indicates reliable extraction of watermarks, where the proposed framework attains an extraction accuracy of more than 95% under additive noise and filtering scenarios and moderate robustness against geometric attacks such as rotation and scaling. These indicate that the proposed framework is effective and provides imperceptible blind image watermarks based on the quantum approach.
Digital media has always been prone to attacks like unauthorized modification, tampering, interception etc. Current watermarking schemes rely on classical key distribution schemes and hence are vulnerable to attacks like eavesdropping and quantum attacks. A method to integrate BB84 based key generation and DCT based embedding is proposed in this paper. The symmetric keys are generated using BB84 scheme and these keys are then used to encrypt the forensic metadata before embedding. Both key exchange process and watermark verification stages are secured. Under standard forensic processing—JPEG compression (QF 30-90), Gaussian noise(variance 0.01), and 30% cropping—watermarks remained detectable with PSNR exceeding 42 dB and SSIM above 0.98.
To address the problem that existing quantum image watermarking schemes have only a single watermarking mode with weak robustness, in this paper we propose a novel multi-modal quantum watermarking (MMQW) scheme using the generalized model of novel enhanced quantum representation. Our scheme provides four quantum watermarking modes (G_G, G_C, C_C, C_G), covering both types of grayscale and color images for the watermark and the carrier image. To enhance the robustness, we propose the Block Bit-plane Centrosymmetric Expansion (BBCE) method, which utilizes controlled quantum gates to extend the watermark, making our method resistant to noise and geometric attacks. Moreover, we propose a Brightness-based Watermarking Mechanism (BWM) for embedding and extraction. By uniform embedding, BWM not only minimizes the impact on the carrier image but also reduces the visual distortion of the extracted watermark. In the proposed MMQW, we implement three adaptive embedding strategies using controlled quantum gates, each of which is adaptively triggered according to the corresponding modalities. Detailed quantum circuits for quantum computing are provided. To evaluate imperceptibility and robustness of the MMQW, we conduct experiments using high-resolution images from the USC-SIPI dataset. The results show that PSNR of the watermarked image ranges from 36 dB to 56 dB, indicating the high visual quality. The PSNR of the extracted watermark is about 34 dB when the noise density is 0.05, while the PSNR is higher than 48 dB under common quantum rotation attacks, which indicate the high robustness against noise addition and geometric attacks. In addition, the proposed MMQW can resist to cropping attack with cropping percentage up to 55%. A comprehensive comparison with existing state-of-the-art works shows that our method has significant advantages.
Abstract This paper proposes a novel dual-mode quantum image watermarking scheme based on the Generalized model of Novel Enhanced Quantum Representation (GNEQR). The scheme supports both grayscale and color images and provides two embedding modes. A key innovation is the Interleaved Tri-Symmetric Structure (ITSS) mechanism. It combines quantum image scrambling using the Quantum Baker Map (QBM) and reconstruction techniques with quantum error correction (QEC), enhancing robustness against noise and geometric attacks such as cropping and rotation. Additionally, a Quantum Parity Detection (QPD) mechanism is employed to efficiently embed and extract the watermark, substantially improving the visual quality of watermarked images. Experimental results on the USC-SIPI dataset demonstrate that the scheme achieves high visual quality, with PSNR values above 44 dB for watermarked images. Under a salt-and-pepper noise attack with density 0.05, the extracted watermark maintains a PSNR of 42 dB, outperforming the existing schemes. The proposed method also fully resists common rotation attacks and allows effective watermark extraction even when the image is cropped by 55%, confirming its strong robustness against geometric attacks. Overall, this work advances secure and efficient quantum watermarking technologies and addresses key challenges in scalability, robustness, and computational complexity for quantum image processing.
The Flexible Representation of Quantum Images (FRQI) is widely used for encoding images into normalized quantum states for secure processing. However, it suffers from high computational and qubit complexity, limiting its scalability. In addition, quantum comparator models, often employed for qubit comparison, remain vulnerable to geometric attacks such as scaling and rotation and require a slow embedding and extraction rate. To address these challenges, this paper proposes a novel quantum watermarking technique that integrates Quantum Image Representation (QIR) with the Novel Enhanced Quantum Representation (NEQR), offering improved efficiency, robustness, and security for quantum image processing (QIP). The proposed technique initiates watermark qubit (QB) generation by integrating pixel position bits (PPB) with an arithmetic average (AA)-based logic, implemented through a quantum circuit comprising reverse parallel adder (RPA), dividing-4 (D4) models, and controlled-NOT (CNOT) gates. For watermark embedding, the proposed circuit employs RPA, quantum equal (QE) models, CNOT gates, and Pauli-X gates, embedding the watermark via a least significant bit (LSB) strategy within the NEQR-encoded image. The extraction process mirrors the embedding architecture, utilizing reverse parallel subtractor (RPS), QE models, CNOT, and Pauli-X gates to ensure accurate watermark retrieval. Experimental results demonstrate high robustness by achieving the highest peak-signal-to-noise-ratio(PSNR) values of 59.97 decibels (dB) along with a structural similarity index matrix (SSIM) of 0.9999. The proposed framework is validated using IBM Qiskit-based quantum circuit simulation on classical computing hardware and is not intended to demonstrate fault-tolerant quantum hardware execution or provable quantum computational advantage. Rather, the work focuses on simulation-level reversible quantum-circuit modelling for secure image watermarking using NEQR-based image representation and quantum-compatible reversible logic structures. Furthermore, the proposed technique is subjected to a range of image processing attacks, quantum noise and decoherence to rigorously evaluate its resilience and performance. In addition, an AI-assisted attack classification module is incorporated to automatically identify the type of distortion applied to the attacked watermarked images. A lightweight Random Forest classifier is trained to classify different attack categories, including no attack, Gaussian noise, salt-and-pepper noise, median filtering, histogram equalization, JPEG compression, cropping, rotation, and text addition. The proposed AI-assisted module achieves an overall classification accuracy of 96.56%, demonstrating its effectiveness in automatic attack identification and further strengthening the applicability of the proposed quantum watermarking framework for secure image authentication. Resource analysis shows that the watermark generation circuit requires 354 QBs (including 320 for RPA, 10 for D4, and 24 for CNOT), while both embedding and extraction consume 400 QBs each. Theoretical evaluation and empirical validation confirm the technique’s effectiveness in preserving image integrity and confidentiality, establishing it as a viable approach for secure quantum image watermarking in real time applications.
The paper presents a novel hybrid quantum water-marking method designed to enhance the security and robustness of digital media protection. The proposed scheme integrates a two-step encryption process, achieving high security while pre-serving quality. Key features include classical LSB substitution, quantum chaos matrix, and an improved quantum representation, offering superior resistance to attacks. By combining spatial and transform domain encryption, the method overcomes vulner-abilities in singular value decomposition, enhancing robustness and peak signal-to-noise ratio (PSNR). Compared to the latest developments-such as sinusoidal-tent map, multi-scale pattern-based quantum text watermarking (MPQTW), maximum pixel difference, tri-way pixel, and NEQR with Quantum Majority Finder (QMF)-the proposed approach shows an average PSNR improvement of approximately 20 % and a significant reduction in Bit Error Rate (BER) by 15.41 %. Additionally, the method demonstrates strong resistance to various attack types with a correlation two-dimensional (Corr 2D) value near 1, such as 0.994 against salt and pepper noise attacks. These results highlight a significant advancement in quantum watermarking technology, balancing improved perceptual quality, security, and robustness.