Digital Image Sharing by Diverse Image Media

Abstract— Conventional visual secret sharing (VSS) schemes hide secret images in shares that are either printed on transparencies or are encoded and stored in a digital form. The shares can appear as noise-like pixels or as meaningful images; but it will arouse suspicion and increase interception risk during transmission of the shares. Hence, VSS schemes suffer from a transmission risk problem for the secret itself and for the participants who are involved in the VSS scheme. To address this problem, we proposed a natural-image-based VSS scheme (NVSS scheme) that shares secret images via various carrier media to protect the secret and the participants during the transmission phase. The proposed (n, n) - NVSS scheme can share one digital secret image over n 1 arbitrary selected natural images (called natural shares) and one noise-like share. The natural shares can be photos or hand-painted pictures in digital form or in printed form. The noise-like share is generated based on these natural shares and the secret image. The unaltered natural shares are diverse and innocuous, thus greatly reducing the transmission risk problem. We also propose possible ways to hide the noiselike share to reduce the transmission risk problem for the share. Experimental results indicate that the proposed approach is an excellent solution for solving the transmission risk problem for the VSS schemes.

INTRODUCTION 

VISVAL cryptography (VC) is a technique that encrypts a secret image into n shares, with each participant holding one or more shares. Anyone who holds fewer than n shares cannot reveal any information about the secret image. Stacking the n shares reveals the secret image and it can be recognized directly by the human visual system [1]. Secret images can be of various types: images, handwritten documents, photographs, and others. Sharing and delivering secret images is also known as a visual secret sharing (VSS) scheme. The original motivation of VC is to securely share secret images in non-computer-aided environments; however, devices with computational powers are ubiquitous (e.g., smart phones). Thus, sharing visual secret images in computer-aided environments has become an important issue today.

Conventional shares, which consist of many random and meaningless pixels, satisfy the security requirement for protecting secret contents [1]–[4], but they suffer from two drawbacks: first, there is a high transmission risk because holding noise-like shares will cause attackers’ suspicion and the shares may be intercepted. Thus, the risk to both the participants and the shares increases, in turn increasing the probability of transmission failure. Second, the meaningless shares are not user friendly. As the number of shares increases, it becomes more difficult to manage the shares, which never provide any information for identifying the shares. Previous research into the Extended Visual Cryptography Scheme (EVCS) or the user-friendly VSS scheme provided some effective solutions to cope with the management issue [5]–[13]. 

The shares contain many noise-like pixels or display low-quality images. Such shares are easy to detect by the naked eye, and participants who transmit the share can easily lead to suspicion by others. By adopting steganography techniques, secret images can be concealed in cover images that are halftone gray images and true-color images [14]–[16] However, the stego-images still can be detected by steganalysis methods [17]. 

Therefore the existing VSS schemes still must be investigated for reducing the transmission risk problem for carriers and shares. A method for reducing the transmission risk is an important issue in VSS schemes. In this study, we propose a VSS scheme, called the naturalimage–based VSS scheme (NVSS scheme), to reduce the intercepted risk during the transmission phase. Conventional VSS schemes use a unity carrier (e.g., either transparencies or digital images) for sharing images, which limits the practicality of VSS schemes. In the proposed scheme, we explore the possibility of using diverse media for sharing digital images. The carrier media in the scheme contains digital images, printed images, hand-painted pictures, and so on. Applying a diversity of media for sharing the secret image increases the degree of difficulty of intercepting the shares. The proposed NVSS scheme can share a digital secret image over n 1 arbitrary natural images (hereafter called natural shares) and one share. Instead of altering the contents of the natural images, the proposed approach extracts features from each natural share. These unaltered natural shares are totally innocuous, thus greatly reducing the interception probability of these shares. The generated share that is noise-like can be concealed by using data hiding techniques to increase the security level during the transmission phase. The NVSS scheme uses diverse media as a carrier; hence it has many possible scenarios for sharing secret images. For example, assume a dealer selects n 1 media as natural shares for sharing a secret image. To reduce the transmission risk, the dealer can choose an image that is not easily suspected as

THE PROPOSED ALGORITHMS 

A. Feature Extraction Process This section first describes the feature extraction module that extracts feature images from the natural shares. The module which is the core module of the feature extraction process is applicable to printed and digital images simultaneously. 

Then, the image preparation and the pixel-swapping modules are introduced for processing printed images. 

1) The Feature Extraction Module There are some existing methods that are used to extract features from images, such as the wavelet transform.

 However, the appearance of the extracted feature may remain some texture of the original image.

 It will result in decreasing the randomness of the generated share and eventually reduces security of the scheme. 

To ensure security of the proposed scheme, we develop a feature extraction method to yield noiselike feature images from natural images such that the generated share is also a noise-like image. 

Assume that the size of the natural shares and the secret image are w h pixels and that each natural share is divided into a number of b b pixel blocks before feature extraction starts.

 We define the notations as follows: b represents the block size, b even. N denotes a natural share. x, y denotes the coordinates of pixels in the natural shares and the secret image, 1 x w, 1 y h. x1, y1 represents the coordinates of the left-top pixel in each block. px,y ϕ denotes the value of color ϕ, ϕ R, G,B for pixel x, y in natural share N, 0 px,y ϕ 255. 

 Pixel value Hx,y is the sum of RGB color values of pixel x, y in natural share N and Hx,y px,y R px,y G px,y B .

 (1) M represents the median of all pixel values (Hx1,y1 ,..., Hxb,yb in a block of N. F is the feature matrix of N, the element f x,y F denotes the feature value of pixel x, y. If the feature value f x,y is 0, the feature of pixel x, y in N is defined as black. If f x,y is 1 the feature of pixel x, y in N is defined as white. As Fig. 3 shows, the feature extraction module consists of three processes—binarization, stabilization, and chaos processes. First, a binary feature matrix is extracted from natural image N via the binarization process. 

Then, the stabilization balances the occurrence frequency of values 1 and 0 in the matrix. 

Finally, the chaos process scatters the clustered feature values in the matrix. Fig. 3. The block diagr