Enhanced image secret sharing via error diffusion in halftone visual cryptography

Alex, Neena Susan; Anbarasi, L. Jani

doi:10.1109/icectech.2011.5941725

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…It is based on mean squared error (MSE). It is commonly used as a quantitative metric in general image quality assessment and in visual secret sharing [7,8].…”

Section: Objective Image Quality Metricsmentioning

confidence: 99%

“…Compared with PSNR and MSE, the advantage of SSIM, as shown in Eq. (7), is that the relationship among pixel neighbors are taken into consideration. SSIM is treated as another commonly applied tool of quality evaluation in image processing and image encryption [26][27][28].…”

Section: Objective Image Quality Metricsmentioning

confidence: 99%

“…Blackness [5,6] of the reconstruction image was discussed in a few later studies. Other scholars [7,8] have used some well-known image quality metrics such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE) to test the difference between the reconstruction image and secret image. None of these metrics work properly in visual quality measurement for visual secret sharing.…”

Section: Introductionmentioning

confidence: 99%

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A novel quality assessment for visual secret sharing schemes

Jiang

King

2017

EURASIP J. on Info. Security

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To evaluate the visual quality in visual secret sharing schemes, most of the existing metrics fail to generate fair and uniform quality scores for tested reconstructed images. We propose a new approach to measure the visual quality of the reconstructed image for visual secret sharing schemes. We developed an object detection method in the context of secret sharing, detecting outstanding local features and global object contour. The quality metric is constructed based on the object detection-weight map. The effectiveness of the proposed quality metric is demonstrated by a series of experiments. The experimental results show that our quality metric based on secret object detection outperforms existing metrics. Furthermore, it is straightforward to implement and can be applied to various applications such as performing the security test of the visual secret sharing process.

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“…It is based on mean squared error (MSE). It is commonly used as a quantitative metric in general image quality assessment and in visual secret sharing [7,8].…”

Section: Objective Image Quality Metricsmentioning

confidence: 99%

Section: Objective Image Quality Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel quality assessment for visual secret sharing schemes

Jiang

King

2017

EURASIP J. on Info. Security

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“…Alex et al [11] suggested various methods for error diffusion in order to increase the quality of the image in the halftone shares. The halftone visual cryptographic is used to fit snugly the pixels of secret information into previously encoded halftone shares.…”

Section: Using Secret Sharing Mechanism To Secure Imagesmentioning

confidence: 99%

Secure File Sharing in Multi-clouds using Shamir’s Secret Sharing Scheme

Althamary

Al-Kharobi

2016

TNC

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Cloud computing is a significant model for permitting on-demand network access to shared data, software's, infrastructure, and platform resources. However, cloud storage needs a certain level of availability, confidentiality, and integrity. Information sensitivity and value encourage users to select a highly secure protocol. This work proposes a new mechanism to increase the user trust in cloud computing using the secret sharing technique. The proposed algorithm is using Base64 encoding to convert any file type to ASCII strings. Base64 strings do not need any extra process to be compressed and this can speed up the share building process. Each string is used to produce N shares (using Shamir Secret Sharing Scheme) where each share is stored in a separate location in the cloud.

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“…The chosen matrix defines the color of the m subpixels in each one of the shares. Based on the above principle, many papers have explored various aspects: general access structure [3][4], pixel expansion [5][6], contrast [7], randomness [8], multiple secrets [9], color image [10], cheating prevention [11][12], meaningful shares [13][14] and other issues [15][16]. Different from the above-mentioned schemes, which are now called deterministic schemes, a probabilistic scheme, which was introduced by Yang [17] to relieve the concern of pixel expansion, recover the secret pixel with a certain probability instead of a deterministic difference.…”

Section: Introductionmentioning

confidence: 99%

Improved Contrast for Threshold Random-grid-based Visual Cryptography

2018

KSII TIIS

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Pixel expansion and contrast are two major performance parameters for visual cryptography scheme (VCS), which is a type of secret image sharing. Random Grid (RG) is an alternative approach to solve the pixel expansion problem. Chen and Tsao proposed the first (k, n) RGbased VCS, and then Guo et al., Wu et al., Shyu, and Yan et al. significantly improved the contrast in recent years. However, the investigations on improving the contrast of threshold RG-based VCS are not sufficient. In this paper, we develop a contrast-improved algorithm for (k, n) RG-based VCS. Theoretical analysis and experimental results demonstrate that the proposed algorithm outperformers the previous threshold algorithms with better visual quality and a higher accuracy of contrast.

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Enhanced image secret sharing via error diffusion in halftone visual cryptography

Cited by 20 publications

References 7 publications

A novel quality assessment for visual secret sharing schemes

A novel quality assessment for visual secret sharing schemes

Secure File Sharing in Multi-clouds using Shamir’s Secret Sharing Scheme

Improved Contrast for Threshold Random-grid-based Visual Cryptography

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