Privacy Protection of Digital Images Using Watermarking and QR Code-based Visual Cryptography

Arora, Akanksha; Garg, Hitendra; Shivani, Shivendra

doi:10.1155/2023/6945340

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…Analyzing the reviewed and other available literature, the following notes can be observed: a) most of the proposed ANN-based watermarking schemes has a lack of high visual quality. The watermarking imperceptibility was at moderate level with PSNR within the range of [37][38][39][40][41][42][43][44] dBs.…”

Section: Related Workmentioning

confidence: 99%

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Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Al-Otum

2023

Preprint

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Image watermarking technologies have been developed to meet the concerns in multimedia copyright protection caused by the tremendous demand on multimedia sharing. In this work, a novel color image watermarking scheme is proposed for copyright protection applications. The developed scheme employs the wavelet packets decomposition (WPD) along with utilizing the back-propagation feed-forward neural networks (BPFNN). Here, a BPFNN-based color image watermarking using WPD decomposition approach is proposed and is denoted as FNN-WPD. The produced WPD-packets, from each of the RGB color channels, are reordered into chains having specific spatial- and frequency-based features. These chains are used for mark embedding and extraction using the BPFNN construction. Simulations have shown superior imperceptibility (PSNR ≥ 60 dB) and improved high robustness against incident and malicious attacks, while exhibiting low computational cost, especially at the extraction stage. Moreover, the scheme has been extended to be used for QR-code embedding with very promising robustness performance.

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Section: Related Workmentioning

confidence: 99%

“…Structural Similarity Index (𝑆𝑆𝐼𝑀). To evaluate the accuracy of the extracted watermark quality, two measures were used: the BPFNN accuracy (𝐴𝐶), and the error bitrate (𝐵𝐸𝑅) between the original and extracted watermarks (Logo/QR) [41]. These measures are defined as follows:…”

Section: Image Dataset and Implementationmentioning

confidence: 99%

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Al-Otum

2023

Preprint

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A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

Holla,

Suma

2024

Int. j. inf. tecnol.

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Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of $$112\times$$ 112 × for $$256 \times 256$$ 256 × 256 color images.

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Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

Rong,

Shravage,

Selva Mary

et al. 2024

Meas. Sci. Technol.

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Visual Cryptography (VC) has emerged as a vital technique in the information security domain, with the fundamental purpose of securing 2-Dimensional (2D) image content through encryption and facilitating secure communication. While traditional VC has been instrumental in safeguarding data, it often falls short in maintaining image quality and semantic accuracy upon reconstruction. To address these limitations, this research encompasses the development of an Enhanced Semantic Visual Cryptography (ESVC) model, which aims to refine the encryption process while ensuring the semantic integrity of the images. The ESVC model introduces a new approach that merges visual cryptography with artificial intelligence to enhance 2D image encryption and decryption. The novel aspect of this research lies in the integration of AI-driven reinforcement learning to increase the quality of the 2D image by measuring the errors between the original secret image and the reconstructed image. This innovative framework is tailored for the secure transmission of 2D grayscale images, ensuring the preservation of semantic integrity while measuring and minimizing image quality loss. By integrating reinforcement learning algorithms with a measurement of error reduction protocol, the model promises robust encryption capabilities with enhanced resilience against a plethora of cyber threats, thereby elevating the standard for secure image communication. Empirical evaluation of the ESVC model yields promising results, with the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images achieving impressive values between +39 and +42 decibels (dB). These findings underscore the ESVC model's capability to produce high-fidelity decrypted images, significantly surpassing traditional VC methods in both security and image quality. The research findings illuminate the potential of merging AI with visual cryptography to achieve a harmonious balance between computational efficiency and encryption strength, marking a significant advancement in the domain of visual data protection.

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Privacy Protection of Digital Images Using Watermarking and QR Code-based Visual Cryptography

Cited by 9 publications

References 20 publications

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

Color image copyright protection based on watermarking using machine learning in the wavelet-packets domain

A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

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