[ieee 2012 international conference on communication, information & computing technology...

Extended Visual Cryptography for Color Images Using Coding Tables

Meera Kamath, Arpita Parab, Aarti Salyankar, Surekha Dholay Computer Engineering Department, Sardar Patel Institute of Technology

Mumbai-400058 [email protected], [email protected], [email protected], [email protected]

Abstract— Visual cryptography (VC) schemes encrypt a secret image into two or more cover images, called shares. The secret image can be reconstructed by stacking the shares together, with no complex cryptographic calculations. This paper proposes a new VC scheme for color images. The shares generated are similar to the cover images, thereby reducing the suspicion of data encryption. The proposed scheme makes use of Jarvis error filter, a key table and specialized tables for coding. High visual quality is achieved as up to 50 percent of the secret image can be recovered.

Keywords- Visual Cryptography, Key Table, Coding table, Jarvis Error Filter, Meaningful Share, Secret Image, Cover Image, Stacking

I. INTRODUCTION Recent advancements in Internet technologies have

enabled information sharing and have brought the world closer. At the same time security concerns have grown proportionally. This has led to organizations, institutions and individuals spending exorbitant amounts of money to secure their data.

Naor and Shamir proposed a “(k,n)-threshold visual secret sharing scheme” in the year 1994, which is now commonly referred to as Visual Cryptography(VC) [1]. The major feature of their scheme is that the secret image can be decrypted simply by the human visual system. Thus no knowledge of cryptography is required when a user uses a system employing visual cryptography. Each share looks like a collection of random pixels and appears meaningless by itself. The generated shares alone do not reveal anything about the secret image [2]. Hence the security level of the secret image is enhanced.

The VC scheme proposed by Naor and Shamir serves as a basic model and has now been adapted to work with color images. Recent efforts have resulted in extended visual cryptographic schemes where the secret image is hidden behind cover images (meaningful shares).

VC can be used in many applications, which include information hiding, transmitting financial documents (VCRYPT) [3], banking applications [4], remote electronic voting applications [5] for authentication and validation. More recent applications are in the field of biometrics such as face privacy [6], iris authentication [7] and fingerprint scanning [8].

This paper is organized as follows. Section II briefly describes the existing VC schemes. Section III discusses at length our proposed algorithm for Extended Visual Cryptography for Color Images. Experimental results are given in Section IV. Finally, the paper is concluded in Section V.

II. EXISTING SCHEMES Visual cryptography is a unique cryptographic technique

which combines the mechanisms of secret sharing and traditional cryptography. It is the pioneered work of Moni Naor and Adi Shamir, and was proposed in 1994. Visual cryptography (VC) schemes hide the secret image into two or more images which are called shares. The secret image can be recovered simply by stacking the shares together without any complex computation involved. The shares are very safe because separately, they reveal nothing about the secret image.

A. Basic Schemes The basic model of visual cryptography proposed by Naor and Shamir accepts a binary image ‘I’ as the secret image, and divides it into ‘n’ number of shares. Each pixel of image ‘I’ is represented by ‘m’ sub pixels in each of the ‘n’ shared images. Stacking of shares reveals the secret image but increases the size by ‘m’ times. The various black and white visual cryptography schemes [9] can be summarized as follows:

1) 2 out of 2 scheme: In this, the secret image is distributed on two shares which are both required for the decryption process [9]. This is depicted in Figure 1.

Figure 1. Working of (2,2) Visual Cryptography scheme

2012 International Conference on Communication, Information & Computing Technology (ICCICT), Oct. 19-20, Mumbai, India

978-1-4577-2078-9/12/$26.00©2011 IEEE 1

This scheme can be realized by using either 2 subpixels or 4 subpixels to represent each pixel of the secret image as explained below.

a) 2 sub pixels: Each pixel is subdivided into one black and one transparent (white) sub pixel as shown in Figure 2 [10].

Figure 2. (2, 2) 2 Subpixels

b) 4 sub pixels: Each pixel is subdivided into four sub pixels, two black and two transparent (white) ones as shown in Figure 3 [2].

Figure 3.(2,4) 2 Subpixels

2) n out of n scheme: In an n out of n scheme the secret message is distributed on n transparencies. Superimposing i transparencies with i < n will not reveal any information of the secret image [9]. There exist two possible ways to construct an n out of n scheme by using 2n subpixels or 2n-1 subpixels.

3) k out of n scheme: Splitting of the secret message into n

shares out of which any k shares are required for decryption. Contrary to the n out of n scheme, not all n transparencies are required for the decryption in this case k < n [9].

B. Halftone Visual Cryptography A severe limitation of the scheme proposed by Naor and Shamir was the fact that the shares generated consisted of random patterns of black and white pixels. This could raise suspicion of data encryption. Mizuho Nakajima and Yasushi Yamaguchi proposed Extended visual cryptography for natural images generating meaningful binary images as shares. However these meaningful shares were of poor quality which again increased the suspicion of data encryption. Zhi Zhou, Gonzalo R. Arce, and Giovanni Di Crescenzo proposed halftone visual cryptography which increased the quality of the meaningful shares [10]. Halftone visual cryptography is based on the principle of void and cluster dithering. Halftoning is the process of using

patterns of pixels of varying size and color to give the illusion of various shades. Halftoning is done as the human eye records only the overall intensity of the image rather than the fine details [11]. Most common halftoning methods are classical screening, dithering with blue noise, direct binary search, error diffusion [10].

C. Visual Cryptography for Color Images F Liu, C.K. Wu and X.J. Lin proposed a new approach on visual cryptography for color images [12] to improve the quality of the decoded image. They proposed three approaches as follows:

The first approach used larger pixel expansion to print the color of the secret image on the share, similar to basic model. However this reduced the quality of the decoded color image.

The second approach converted a color image into three binary images on the three color channels (red, green, blue or equivalently cyan, magenta, yellow), respectively, and then applied the black and white VC scheme to each of the color channels. Although the pixel expansion decreased, the quality of the image did not improve.

The third approach utilized the binary representation of the color of a pixel to encrypt the secret image at the bit-level. The quality of image improved but now devices were required for decryption.

III. PROPOSED ALGORITHM There are three steps in our algorithm:

1) Color Halftone Transformation 2) Encoding and Generation of Shares 3) Decryption

Each of these steps is explained in detail below:

A. Color Halftone Transformation The sender inputs four cover images and one secret image CA, CB, CC, CD and SI respectively. Each image is of size NxN pixels. In this step the five color images CA, CB, CC, CD and SI are transformed into respective halftone images IA, IB, IC, ID and IS. The size of the halftoned images is also NxN pixels.

Each input image is decomposed into three constituent planes red, green and blue. Then the halftone technique is applied to each of these planes. By combining these three halftoned planes, a color halftone image is generated. Halftoning is performed using error diffusion. The error diffusion algorithm uses Jarvis filter as shown in Figure 4.

Figure 4. Jarvis Error Filter


978-1-4577-2078-9/12/$26.00©2011 IEEE 2

B. Encoding and Generation of Shares A Key Table and two types of Coding Tables—Cover Table (CT) and Secret Table (ST) are used to encode the secret image into the cover images. These encoded cover images are meaningful shares and can be transmitted securely. The sender has the option to select two (or more) of the four shares generated for transmission. The secret image is obtained when the receiver stacks the shares. The steps used in encoding are:

1) Key Generation 2) Cover Images Encoding 3) Secret Image Encoding 4) Generation Of Shares

1) Key Generation: One block out of the six combinations shown in Key Table(Table 1) is randomly chosen for each pixel of Secret Image. Thus a Key Image K of size 2Nx2N pixels is formed.

2) Cover Image Encoding: Each pixel of the cover image is expanded into a 2x2 block chosen from the CT as shown in Table 2.

Let the pixel coordinates of the cover image be (i,j). The cover table is chosen corresponding to the key block(Table 1) with upper left coordinates (2*i,2*j) in the key image.

Eg. If the Key Block combination is, then CT1 is chosen.

Thus each of the four halftoned cover images IA, IB, IC and ID of size NxN pixels is transformed into intermediate images OA, OB, OC and OD of size 2Nx2N pixels.

3) Secret Image Encoding Secret Table is used to encode the pixels of the secret image. ST is also chosen based on Key Block. There are six ST’s one for each Key Block.

Eg. If the Key Block combination is, then ST1 is chosen.

Each pixel of the secret image is represented by two blocks, OS1 and OS2 in the ST as shown in Table 3. The blocks are created such that, on stacking them the resulting block will have two pixels of the same color as the secret image pixel. The other two pixels will be white.

Thus the halftoned secret image IS of size NxN pixels is transformed into intermediate images OS1 and OS2 of size 2Nx2N pixels.

4) Generation of shares

Two of the four halftoned and encoded cover images (OA, OB, OC and OD) are randomly chosen, say OA and OB. One block from OA and one block from OB are then ORed with the two blocks from OS1 and OS2 at corresponding positions, to produce blocks in shares, Share1 and Share2. In this case, Share3 and Share4 will have the same block as OC and OD.

The above step is repeated for each block of secret image resulting in generation of four shares Share1, Share2, Share3, and Share4 of size 2Nx2N pixels. An example of share generation is shown in Figure 5.

Figure 5. Example of Share Generation

TABLE 2. COVER TABLE (CT)

TABLE 1. KEY TABLE (KT)


978-1-4577-2078-9/12/$26.00©2011 IEEE 3

TABLE 3. SECRET TABLE (ST)

C. Decryption

In the decryption process, we stack two or more shares along with the Key Image to reconstruct the secret image. Figure 6 shows an example of decryption with blocks from two shares, Share1 and Share2 and the corresponding block from the Key Image. The block of the stacked image produced contains two subpixels of the same color as the pixel of the secret image and the other two subpixels are black. Since two subpixels out of four in each block will always be of the same color as the pixel of the secret image, 50% of the secret image is retained in the final reconstructed image.

Figure 6.Example of Decryption


978-1-4577-2078-9/12/$26.00©2011 IEEE 4

IV. EXPERIMENTAL RESULTS The four cover images and secret image used to test our

algorithm are shown in Figure 7. All images are of size 512x512 pixels. Share1, Share2, Share3 and Share4 generated are 1024x1024 pixels each as shown in Figure 8. Key Image is also of size 1024x1024 pixels and is shown in Figure 9. By stacking all four shares and Key Image together, the secret image is revealed as shown in Figure 10.

As seen in the experimental results, our new scheme successfully encrypts the secret image inside the meaningful shares, and later the secret image can be recovered simply by stacking the shares and the Key Image together. The efficiency of our scheme is 50% i.e. half of the pixels of the secret image are black and the other half are of the same color as the original secret image.

There are two main features that improve the security of our algorithm. First is the Key Table that ensures that the pixels of the secret image are encoded in different ways. Second, during the generation of shares, two out of the four halftoned and encoded cover images are randomly chosen, which ensures that any share by itself, or a single share along with the Key Image will not reveal the secret image.

The following objective metrics [13] have been used for comparison between the original secret image and the reconstructed secret image:

i) Mean Square Error (MSE): It measures the average of the square of the error. The error is the amount by which the pixel value of original image differs from the pixel value of decrypted image.

Where x(i,j) represents the original image, y(i,j) is the decrypted image and (i,j) represent the pixel positions of the MxN image. Here, M and N are the height and width of image respectively.

ii) Peak signal to noise ratio (PSNR): It is the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. PSNR is usually expressed in terms of the logarithmic decibel. PSNR is given by

iii) Normalized Correlation (NC): It measures the

similarity representation between the original image and decrypted image.

Comparing secret image (Figure 7(e)) and reconstructed secret image (Figure 10) using the above metrics, we get

PSNR = 1.61266 and Normalized Correlation= 0.41798.

Figure 7. Cover Images and Secret Image

Figure 8. Shares

Figure 9. Key Image Figure 10. Secret image obtained by stacking four shares and Key Image

(a) Share 1 (b) Share 2

(c) Share 3 (d) Share 4

(a) Cover Image CA (b) Cover Image CB

(c) Cover Image CC (d) Cover Image CD

(e) Secret Image SI


978-1-4577-2078-9/12/$26.00©2011 IEEE 5

The proposed scheme has also been tested for facial images, and hence can be used for military and police applications such as face privacy [6]. The results are shown in Figure 11. The images have been taken from the Indian Face Database [14].

V. CONCLUSION In this paper, we have proposed a new VC scheme for

color images using meaningful shares. Like the existing schemes, the size of the shares produced and final image after stacking are twice the size of original image. However, the visual quality achieved by our algorithm is higher. The Key Table and Image Encoding procedure used considerably improves the security by increasing the randomness. Therefore, the probability of the secret image being guessed is very low.

VI. REFERENCES

[1] M.Naor and A. Shamir “Visual cryptography”, Advances in Cryptology EUROCRYPT ’94. Lecture Notes in Computer Science, (950):1–12, 1995.

[2] Hsien-Chu Wu, Hao-Cheng Wang, and Rui-Wen Yu, “Color Visual Cryptography Scheme Using Meaningful Shares,” ISDA’08, Vol. 3, pp. 173-178, Nov. 2008.

[3] L. W. Hawkes, A. Yasinsac and C. Cline, “An Application of Visual Cryptography to Financial Documents,” Technical Report TR001001, Florida State University, 2000.

[4] Jayanta Kumar Pal, J. K. Mandal and Kousik Dasgupta, “A (2, N) Visual Cryptographic Technique For Banking Applications,” International Journal of Network Security & Its Applications (IJNSA), Vol.2, No.4, pp-118-127, October 2010.

[5] N.Paul, D.Evans, A. Rubin, and D. Wallach, “Authentication for Remote Voting,” Workshop on Human- Computer Interaction and Security Systems, April 2003.

[6] A. Ross and A. Othman, “Visual Cryptography for Face Privacy,” Proc. of SPIE Conference on Biometric Technology for Human Identification VII, April 2010.

[7] P.S. Revenkar, A. Anjum and W.Z. Gandhare, “Secure Iris Authentication Using Visual Cryptography,” IJCSIS, Vol. 7, No.3, pp. 217-221, 2010.

[8] Y.V.S. Rao, Y. Bhagwati and C. Singh, “Fingerprint Based Authentication Application Using Visual Cryptography Methods,” TENCON-2008-2008 IEEE Region 10 Conference, Nov. 2008.

[9] Debashish Jena and Sanjay Kumar Jena, “A Novel Visual Cryptographic Scheme”, ICACC’09, pp. 207-211, 2009.

[10] Nitty Sarah Alex and L. Jani Anbarasi, “Enhanced Image Secret Sharing via Error Diffusion in Halftone Visual Cryptography,” ICECT’11, Vol. 6, pp. 393-397, April 2011.

[11] Nagaraj V. Dharwadkar, B. B. Amberker and Sushil Raj Joshi, “Visual Cryptography for Color Image using Color Error Diffusion”, ICGST-GVIP Journal, ISSN: 1687-398X, Volume 10, Issue 1, February 2010.

[12] Chandramathi S., Ramesh Kumar R., Suresh R. and Harish S., “An Overview of Visual Cryptography,” IJCI’10, Vol. 1, Issue 1, pp. 32-37, 2010.

[13] C. Sasivarnan, A. Jagan, Jaspreet Kaur, Divya Jyoti, and Dr. D.S. Rao, “Image Quality Assessment In Spaatial Domain,” IJCST, Vol. 2, Issue 3, September 2011.

[14] Vidit Jain, Amitabha Mukherjee. The Indian Face Database. http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/, 2002.

Figure 11(a-j). Application of scheme for face privacy

(f) Share 1 (g) Share 2

(h) Share 3 (i) Share 4

(a) Cover Image CA (b) Cover Image CB

(c) Cover Image CC (d) Cover Image CD

(e) Secret Image SI

(j)Secret image obtained by stacking shares and key image


978-1-4577-2078-9/12/$26.00©2011 IEEE 6