Secure grayscale image communication using significant visual cryptography scheme in real time applications

Mary, G. Selva; Kumar, Sanjay

doi:10.1007/s11042-019-7202-7

Cited by 18 publications

(4 citation statements)

References 24 publications

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“…RSVs are obtained from icimg, which is obtained by embedding ICV values, sides of the polygon, and coordinates of x and y, respectively, in SSI. RSVs are obtained from the equations (1)- (5).…”

Section: Using Cover Imagesmentioning

confidence: 99%

A study on secured encryption of medical images using significant visual cryptography

H M,

Rao,

Khan

et al. 2024

Eng. Res. Express

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The medical images of people are important and sensitive and cannot be shared with the public considering privacy measures. Maintaining the confidentiality of the medical image is essential, and leakage of such information can cause great loss. Therefore, the information has to be secured while being transferred through a third party, which can be any network medium. Thus, there is a need for developing a robust encryption algorithm. These algorithms improve the security of the ongoing pictures by compromising the nature of the picture and utilizing complex calculations. Algorithms that work on the nature of the picture by utilizing complex cycles, for example, error diffusion, halftoning, wavelet transform, and dithering, lead to time complexity. Thus, a compact and efficient cryptographic algorithm is proposed with fewer mathematical computations that ensure the secured transmission and reception of medical images through the medium using Significant Visual Cryptography (SVC). In SVC, initially, the quality of the secret images (SI) is improved by using the Error Abatement Technique (EAT). The output of EAT is used to generate random share values, which are then implanted in cover pictures. The shares that are transmitted do not reveal the secret information present in the original image because of the steganography features involved in this technique. The integrated check value (ICV) is calculated over the region of interest (ROI) at the encryption and decryption sides to provide additional security. Quality and security analyses have been carried out to ensure the robustness of the algorithm. The detailed study proved that the proposed algorithm beat the constraints of the current calculations. The concept of checking the integrity value and steganography features enhanced the effectiveness of the algorithm.

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Section: Using Cover Imagesmentioning

confidence: 99%

A study on secured encryption of medical images using significant visual cryptography

H M,

Rao,

Khan

et al. 2024

Eng. Res. Express

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“…No. of secret Complexity [1] 1995 Random Binary 1 Low [2] 2001 Random Binary 1 Low [3] 2003 Random Color 1 Low [4] 2006 Random Grayscale 1 Low [5] 2021 Random Binary n High [6] 2021 Random Grayscale 1 High [7] 2024 Random Grayscale n High [8] 2023 Meaningful Color 1 Low [9] 2016 Random Grayscale 1 High [10] 2023 Random Color 1 High [11] 2022 Random Half-tone 2 High [12] 2020 Meaningful Grayscale 1 High [13] 2017 Random Grayscale 2 High [14] 2018 Meaningful Binary 2 Low [15] 2017 Random Binary 2 High [16] 2022 Meaningful Color 2 High [17] 2020 Random Color 1 Low subsequent research expanding its application beyond binary images to include grayscale and color images, thereby broadening the spectrum of its applicability and complexity [2]. Another significant innovation in VC is halftone VC, which utilizes dithering techniques to create shares that, when stacked, approximate the grayscale or color tonality of the original image.…”

Section: Type Of Imagementioning

confidence: 99%

“…For PDM analysis, three specific metrics are considered: PSNR, Mean square error (MSE), and mean absolute error (MAE). In the realm of HVM, two metrics are utilized: Universal image quality index (UIQI) and Structural Similarity (SSIM) Index [8][9][10][11][12]. The values are measured using equations ( 29)- (31).…”

Section: Improved Image Qualitymentioning

confidence: 99%

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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“…This may lead to severe damages for the IT society instead of supporting continuous development. Even though missing or inadequate security measures may not cause severe breakdowns initially, the IT society gradually will lose trust, resulting in a devastating decline in development [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Toward a Sustainable Cybersecurity Ecosystem

et al. 2020

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Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community.

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Secure grayscale image communication using significant visual cryptography scheme in real time applications

Cited by 18 publications

References 24 publications

A study on secured encryption of medical images using significant visual cryptography

A study on secured encryption of medical images using significant visual cryptography

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

Toward a Sustainable Cybersecurity Ecosystem

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