A Note on Multiple Secret Sharing Using Chinese Remainder Theorem and Exclusive-OR

Abstract

This paper reviews the former existing scheme on (n, n)-multiple secret sharing (MSS) for color images along with its slight limitation. This scheme generates a set of n shared images from a set of n secret images using the Chinese remainder theorem (CRT) and Boolean exclusive-OR (XOR) operation. This scheme works well if the number of secret images n is even number. However, the former scheme has a slight problem while the number of secret images n is an odd number. This paper proposes a new technique to overcome this problem by introducing symmetric and transferred masking coefficients to generate a set of shared images. To further improve the security level of the proposed method, a set of secret images is first transformed with hyperchaotic scrambling method before generating shared images. The security of the proposed (n, n)-MSS can also be increased by merging a shared color image into 2-D matrix representation. As documented in the experimental results, the proposed method offers a promising result on (n, n)-MSS scheme regardless of the number of secret images n is odd or even number. In addition, the proposed method outperforms the former existing (n, n)-MSS schemes in terms of quantitative measurements. INDEX TERMS Chinese remainder theorem, exclusive OR, secret sharing, symmetric masking coefficient, transferred masking.

Swarm Intelligence for Additive White Gaussian Noise Level Estimation

“…Figure 3 presents the relationships between P A (M ) and θ GT for various image sizes. This figure confirms that the noise level θ can be considered as a simple linear function with P A (M ), as indicated in (12). Subsequently, the noise levels were estimated for all images in each training set.…”

“…However, such noise level estimation requires a scalar value of α. In a previous scheme [6], the value of α was simply computed by applying the LSF method in (12) to a set of training images, where different image sizes yield different scalar values.…”

“…The value of α * is different for different image sizes because the computation of P A (M ) and P B (M ) depend on the matrix rank, meaning M = 3 4 r. Table 2 lists the optimal values of α * for various methods. The symbol α * LSF denotes the optimal α obtained from LSF for the training image set, as formulated in (12). α * P SO is the optimal α obtained from PSO and its variants.…”

“…The proposed method overcomes the challenge of image denoising when prior information regarding noise levels is unavailable. The proposed method can be further extended for multiple secret sharing [12,13], progressive secret sharing [14], vehicle verification [15], and other image processing and computer vision tasks. This remainder of this paper is organized as follows.…”

Vulnerability Attacks of SVD-Based Video Watermarking Scheme in an IoT Environment

“…Multimedia data often consists of confidential information or secret valued messages. Several attempts have been developed to protect multimedia data, such as digital image watermarking [1]- [19], secret sharing [20]- [22], etc. These aforementioned methods show promising results in securing multimedia data.…”