VISUAL CRYPTOGRAPHY for COLOR IMAGES … CRYPTOGRAPHY for COLOR IMAGES USING ERROR DIFFUSION AND...

VISUAL CRYPTOGRAPHY for COLOR IMAGES USING ERROR

DIFFUSION AND PIXELSYNCHRONIZATION

Pankaja Patil

Department of Computer Science and EngineeringGogte Institute of Technology, Belgaum, Karnataka

Bharati Pannyagol

Department of Computer Science and EngineeringGogte Institute of Technology, Belgaum, Karnataka

ABSTRACT-Visual Cryptography (VC) is not sufficient in terms of providing color meaningful shares with high visual quality. This paper produces visual cryptography encryption method for color images which use the error diffusion and visual information pixel (VIP) synchronization techniques to generate meaningful color shares with high visual quality. Low frequency differences between the input and output images are minimized in Error diffusion method and consequently it produces pleasing halftone images to human vision. Synchronization of the VIPs across the color channels improves visual contrast of shares. Comparison of Jarvis halftone with Floyd halftone shows good result of Jarvis method.

Keywords—Color meaningful shares, halftoning, error diffusion, visual cryptography (VC).

I INTRODUCTION

Visual Cryptography (VC) is a data security technique which allows visual information (pictures, text, etc.) to be encrypted in such a way that decryption operation does not require a computer. Visual Cryptography (VC) for black and white was first formally introduced by Naor and Shamir [1]. In which one secret binary image is cryptographically encoded into n shares of random binary patterns. The n shares are distributed amongst group of n participants, one for each participant. No participants can retrieve any information from his own transparency, but any k or more participants can visually reveal the secret image by polling there transparencies together. The secret cannot be decoded by any k-1 or less participants, even if higher computational power is available to them. In VC the decryption process requires only human visual system. This property makes visual cryptography especially useful for the low computation load requirement.

VC scheme has been applied to many applications. Apart from the obvious applications to information hiding, there are many applications of VC [2, 3], which include general access control [4]. VC can also be used in a number of other applications such as threshold cryptography, electronic cash, private multiparty computations and digital electronics etc.

International Journal of Latest Trends in Engineering and Technology (IJLTET)

Vol. 1 Issue 2 July 2012 1 ISSN: 2278-621X

Fig.1: Construction of (2, 2) VC scheme

Generally, the black-and-white (2, 2) visual cryptography decomposes every pixel in a secret image into a 2×2 block in the two transparencies according to the rules in figure 1, two of them black and white. If pixel is white (black) one of the above six columns of figure 1 is chosen to generate Share1 and Share2. Then, the characteristics of two stacked pixels are: black and black is black, white and black is black, and white and white is white. Therefore, when stacking two transparencies the blocks corresponding to black pixels in the secret image are full black and those corresponding to white pixels are half-black-and-half-white. As concern to information security, one of the six columns is selected with equal probability.

(a) Binary secret image. (b) Encrypted share 1.

(c) Encrypted share 2. (d) Decrypted secret message.

Fig.2: Example of 2-out-of-2 scheme. The secret image is encoded into two shares showing random patterns. The decoded image shows the secret image with 50% contrast loss.

Figure 2 shows an example of a simple (2, 2)-VC scheme with a set of subpixels shown in figure 1. Figure 2(a) shows a secret binary message, Figure 2(b) and (c) depict encrypted shares for two participants. Stacking these two shares leads to the output secret message as shown in figure 2(d).

Little research has been carried out on VC, a more general method for VC scheme is based upon general access structure [4]. The access structure is a specification of qualified and forbidden subsets of shares. The participants in a qualified subset can recover the secret image while the participants in a forbidden subset cannot. But this technique gives good result on binary images. In extended visual cryptography (EVC)[5] method, a shares contain not only the secret information but are also some meaningful binary images are developed. In this method Hypergraph colorings are used for constructing meaningful binary shares. Since, hypergraph colorings are constructed by random pixels distribution, the resultant binary shares contain strong white noise leading to insufficient results. A VCS for color images based upon an additive color



mixing [6] method is introduced. In this scheme, each pixel is expanded by a factor of three, which will increase the size of encrypted shares.

This paper introduces a color VC encryption method to generates meaningful shares. It based on two fundamental concepts used in the generation of shares they are error diffusion[7] and pixel synchronization[8]. Error diffusion is a procedure that produces pleasing halftone images to human vision. Synchronization of the pixels of secret image and covering images across the color channels improves visual quality of shares. Visual Information Pixel (VIP) synchronization prevents colors and contrast of original shares from degradation even with matrix permutation.

This paper is organized as follows. Section II describes the proposed method which uses Error diffusion and VIP Synchronization. Section III shows experimental results of the new method and comparisons of Error diffusion methods. Finally, we conclude this paper in Section IV.

II . IMPLEMENTATION

System is designed into 2 phases. The first phase generates shares by using the error diffusion [11] algorithm and Pixel Synchronization. The Figure 3 explains the working of the system.

Fig.3 Block Diagram of System Design

A. HalftoningError diffusion [7],[9] produces halftone images of much higher quality than other halftone. It quantifies each pixel using a neighborhood operation. A schematic diagram of error diffusion method is given in figure 4. The error diffusion scans the image one row at a time and one pixel at a time. The current pixel is compared to a threshold (127) value. If it is above the value a white pixel is generated in the resulting image. If the pixel is below the half way value, a black pixel is generated. The generated pixel is either full bright, or full black.

Fig.4: Error Diffusion Block Diagram

N covering

Secret Img

Share Generation

Halftoning

VIP synchronization

StackingSecret image



Fig.5: Two error diffusion weight matrixes (a) Jarvis, Judice, and Ninke (b) Floyd and Steinberg

Error is calculated which is the difference between original image and halftone image. The error is then added to the next pixel in the image and the process repeats. To which neighbor and how this error is pushed is decided by an error diffusion matrix.

Algorithm: Jarvis error diffusion halftoning

1: procedure JARVIS ERROR DIFFUSION (g)

2: for i=1,……..n do

3: for j=1,………….m do (This algorithm goes through all pixels in the original image, normally starting from the pixel up to the left and then goes through all pixels from left to right and up down).

4: if f[ij] >127 then

b[ij]=1

else

b[ij]=0

5: Since the pixel value in f, which is a real number between 0 and 255, has been replaced by 0 or 1 in b and “error” has been calculated.

e= f - b(i,j)The “error” is the difference between the pixel value in f and b at that position.

6: The error occurred at the position (i, j) is weighted by 7/48 and added to the pixel value at (i+1, j). The same error is weighted by 5/48 and added to the pixel at (i+1, j+1) and so on.

After the error has been diffused the pixel value of the next position is compared to the threshold and the same process continues until all pixels have been met.

7: end for 8: end for

9: end procedure

Floyd and Steinberg error diffusion method follow the same algorithm except that while distributing error it uses Floyd and Steinberg matrix as shown in figure 5(b).

B.VIP Synchronization



Visual Information Pixel (VIP) is pixel on the encrypted shares that have color values of the original images, which make shares meaningful. In the proposed method each subpixel n carries visual information as well as message information ,while other methods in [1] and [5] extra pixels are needed in addition to the pixel expansion n to produce meaningful shares.

Algorithm: VIP synchronization

Input: C1, C2 covering Images of size n x m, Sc secret image of size K1xK2.Output: 2 meaningful shares1: procedure: VIP Synchronization and Matrix Distribution2: for p=1,………..K1 and for q=1,………K2 do3: for the color channel R of the secret image ScR(p,q) do4: if the bit ScR(p,q)=1 then

for i=1,………..K1 do for j=1,……….K2 doif C1(i,j)=C2(i,j) then

Randomly select any one Ci and complement Ci(i,j) end if

5: end for end for6: else if ScR(p,q)=0 then

for i=1,………..K1 do for j=1,……….K2 do

if C1(i,j) thenRandomly select any one Ci and make them equal i.e.C1(i,j)=C2(i,j) or C2(i,j)= C1(i,j)

end if end for end for

end if7: Repeat 3 to 6 for the channel G and Y.8: end for 9: end for10: end procedure

This algorithm takes the input as halftone images which are created by error diffusion method. It decomposes the color images into 3 basic colors (Red, Green, and Blue) and then it executes VIP Synchronization algorithm on each color bit. The output of this block are meaningful shares. Now each bit on share contains information regarding covering image as well as secret image without giving any clue about encryption.

C. Stacking

Decoding does not need any algorithm. The meaningful shares are XORed to reconstruct the secret image by simply human vision system.

III RESULTS and ANALYSIS

The algorithms discussed above are implemented using MATLAB 2008 and higher version on P8600 @ 2.40GHz, 2.92 GB RAM. To test the performance of these algorithms 4 color images belonging to different classes of size 128x128 are used.

A. Results

In this section, we provide some experimental results to illustrate the effectiveness of the proposed method. Example are composed with (2, 2) Color VC, (2,3) Color VC and (3,4) color VC. The secret message of size 128x128 pixels and covering images of size 256x256 in natural colors are provided for the share



generation. Figure 6 to 10 represent the results of each step of the system. Size of images is resized to fit in the paper.

(a) (b) (c) (d) (e)Fig.6 (a) – (d) Covering Input Images of size 256x256 (e) secret input image of size 128x128

(a) (b) (c) (d) (e)

Fig.7 Halftone shares using error diffusion method

All the images are halftone before encryption process. Halftone images create a space so that we can embed secret message into covering image.

A. (2, 2) Visual Cryptography

(a)Share 1 (b) Share 2 (c) Reconstructed secret image

Fig 8 (a)-(b) result of encrypting images (a),(b) and (e) of figure 7. fig.8 (c) Result of stacking (a) and (b) of figure 8.

B. (2, 3) Visual Cryptography



(a)Share1 (b) Share 2 (c) Share 3 (d) Reconstructed secret image

Fig 9 (a)-(c) Result of encrypting images (a),(b) ,(c)and (e) of figure 7.fig 9 (d) Result of stacking (a),(b) and (c) of figure 9

C. (2, 4) Visual Cryptography

(a) Share1 (b) Share2 (c) Share3 (d) Share4 (e) reconstructed image

Fig.10 (a)-(d) Result of encrypting images (a),(b) ,(c),(d) and (e) of figure 7.fig.10 (e) Result of stacking (a),(b) ,(c)and (d) of figure 10

B .Analysis

Table-I,II and III shows the result of 4 sample images of different categories for Jarvis[13] and Floyd and Steinberg[8] error diffusion algorithm for (2,2) ,(2,3) and (2,4) VC schemes ,respectively.

TABLE I: PSNR AND CORRELATION PARAMETERS USED FOR DIFFERENT IMAGES AND HALF TONE METHODS FOR 2 SHARES.

Image Parameter Floyd-Steinberg method Jarvis method

Letter img PSNR 84.9575 89.3672Correlation 0.974538 0.99192

Map Img PSNR 71.7093 79.246Correlation 0.81175 0.986

Chi Img PSNR 77.8475 82.1807Correlation 0.960843 0.990318

Logo Img PSNR 75.4791 78.7595Correlation 0.950232 0.981658

Fig.11 (a) Different images versus PSNR with different halftone methods Fig.11 (b) Different images versus

Correlation Coefficient with different halftone methods.



Figure 11 (a) shows the PSNR values for the sample images. Stacked image produced by Jarvis [13] is very good retains pictorial details as compared to Floyd and Steinberg [8] half tone method. Since, Jarvis halftone method give clearer image compared to Floyd and Steinberg halftone method. Figure 11 (b) shows the correlation values for the sample images. Reconstructed image produced by Jarvis [13] is very much similar to original image as compared to Floyd and Steinberg [8] halftone method. This means there is very small amount of data loss in Jarvis halftone method.

TABLE II: PSNR AND CORRELATION PARAMETERS USED FOR DIFFERENT IMAGES AND HALF TONE METHODS FOR 3 SHARES.

Image Parameter Floyd-Steinberg method Jarvis method

Letter img PSNR 78.8211 76.0964

Correlation 0.891287 0.825342

Map Img PSNR 71.2261 74.2355

Correlation 0.872098 0.971221

Chi Img PSNR 73.8673 75.1725

Correlation 0.935264 0.95069

Logo Img PSNR 74.5543 74.4709

Correlation 0.929823 0.91969

Fig.12 (a): Different images versus PSNR with different halftone methods Fig.12 (b): Different images versus Correlation coefficient with

different halftone methods.

Figure 12 a) and b) shows the PSNR and correlation values for the sample images, respectively. Jarvis halftone method produce clearer image compared to Floyd and Steinberg halftone method. If less information is present secret image then in (2,3) VC Floyd and Steinberg provide clear picture compared to Jarvis.

TABLE III: PSNR AND CORRELATION PARAMETERS USED FOR DIFFERENTIMAGES AND HALF TONE METHODS FOR 4 SHARES.

Image Parameter Floyd-Steinberg method

Jarvis method

Letter img

PSNR 66.8975 74.0314

Correlation 0.142009 0.742706

Map Img PSNR 65.4256 65.9732

Correlation 0.0236446 0.480244

Chi Img PSNR 66.1038 67.4695

Correlation 0.184984 0.701278

Logo Img PSNR 66.9184 70.5797

Correlation 0.101503 0.65802



Fig.13 (a) Different images versus PSNR with different halftone methods Fig 13 (b) shows the correlation values for the sample images

Figure 13(a) and (b) shows the PSNR and correlation values for the sample images, respectively for (2,4) VC. Jarvis halftone method produce clearer image compared to Floyd and Steinberg halftone method.

TABLE IVPSNR PAMETERS USED FOR DIFFERENT IMAGES

AND NO.OF SHARES GENERATED

Images 2 Shares 3 Shares 4 SharesLetter img 89.3672 76.0964 74.0314

Map Img 79.2460 74.2355 65.9732

Chi Img 82.1807 75.1725 67.1217

Logo Img 78.7595 74.4709 70.5797

Fig.14 Different images versus PSNR with different No. of share generation.

Figure 14 shows PSNR value for different VC scheme. Stacked image of (2,2) VC scheme is very good retains pictorial details as compared to another two VC schemes. As we increase the number of covering images performance of Synchronization process is less. As the security point of view (2,2) has more security ratio compared to (2,3) and (2,4) VC.

IV. CONCLUSION AND FUTURE WORK

The proposed system presents an encryption method for color Visual Cryptography scheme with Error diffusion and VIP Synchronization for visual quality improvement. Jarvis and Floyd and Steinberg halftone methods are compared. Jarvis kernel gives better visual quality. For encryption VIP synchronization is used. It hold the original pixels in the actual VIP values to produce meaningful shares. The secret information is revealed by overlapping of meaningful shares.

V. REFERENCES

[1] M. Naor and A. Shamir, “Visual cryptography,” in Proc. EUROCRYPT, 1994, pp. 1–12.

[2] M. S. Fu and O. C. Au, “Joint visual cryptography and watermarking,” in Proc. IEEE Int. Conf. Multimedia Expo, 2004, pp. 975–978.

[3] M. Naor and B. Pinkas, “Visual authentication and identification,” Adv.Cryptol., vol. 1294, pp. 322–336, 1997.

[4] G. Ateniese, C. Blundo, A. D. Santis, and D. R. Stinson, “Visual cryptography for general access structures,” Inf. Comput., vol. 129, no. 2, pp. 86–106, 1996.



[5] G. Ateniese, C. Blundo, A. Santis, and D. R. Stinson, “Extended capabilities for visual cryptography,” ACM Theor. Comput. Sci., vol. 250,pp. 143–161, 2001.

[6] C. N. Yang and T. S. Chen, “Visual cryptography scheme based on additive color mixing,” Pattern Recognit.,vol. 41, pp. 3114–3129, 2008.

[7] S. Gooran, “Digital Halftoning”,Thesis, Linkoping University, Linkoping,Sweden.

[8] InKoo Kang,Gonzalo R.Arce,heung-Kyu Lee, “Color Extended Visual Cryptography Using Error Diffusion,” IEE Trans. On Image processing,vol.20.no.1.2.11

[9] Sadan Ekdemir, Xunxun Wu, “Digital Halftoning Improvements on the Two-by-Two Block Re-placement Method“.333,jan 2011.



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