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Security has become an inseparable issue since information engineering is governing the universe at present. Cryptanalysis is the survey of mathematical techniques and related facets of Information Security such as informations confidentiality, informations Integrity, and of informations hallmark. Ocular cryptanalysis ( VC ) is a secret sharing strategy where a secret image is encrypted into the portions which independently unwrap no information about the original secret image. The beauty of the ocular secret sharing strategy is its decoding procedure i.e. to decode the secret image utilizing human ocular system without any calculation. Naor and Shamir proposed the basic theoretical account of ocular cryptanalysis for natural images. This paper presents a new algorithm for halftone image and by stacking the portions ; the attendant image achieved in same size with original secret image. A new attack is proposed in ocular information concealing utilizing pseudo randomisation and pel reversal attack in all methods.

Keywords-Information Security, Information concealment, Halftone image, Visual cryptanalysis, Secret portion.

Information Hiding In Gray Scale Computer... TOPICS SPECIFICALLY FOR YOU


With the coming epoch of electronic commercialism applications, there is an pressing demand to work out the job of guaranting information security in today ‘s increasing unfastened web environment. The coding methods of traditional cryptanalysis are normally used to protect information security. In traditional cryptanalysis techniques, the informations become rearranged/ permutated after being encrypted and can so be decrypted by a right key. So, there is computational operating expense in decoding procedure. The new and emerging construct of ocular cryptanalysis ( VC ) was introduced by Naor and Shamir in 1994 [ 1 ] , which requires no calculation except the human ocular system to treat decoding. They proposed a basic ( 2, 2 ) ocular cryptanalysis strategy which encodes a given secret image into 2 portions, and reveals the secret image by stacking the portions. The chief advantage of this attack is that it can retrieve a secret image without any calculation. It exploits the human ocular system to read the secret message from some overlapping portions, therefore get the better ofing the disadvantage of complex calculation required in the traditional cryptanalysis.

Related Work

The theoretical account for ocular cryptanalysis is given by Naor & A ; Shamir as follows [ 2 ] :

A printed page of cypher text and a printed transparence, which serves as a secret key. The original clear text is revealed by puting the transparence with the key over the page with the cypher, even though each one of them is identical from random noise. The theoretical account for ocular secret sharing is as follows [ 3 ] . There is a secret image to be shared among n participants. The image is divided into n transparences ( portions ) such that if any m transparences are placed together, the image becomes seeable. If fewer than m transparences are placed together, nil can be seen. Such a strategy is constructed by sing the secret image as a set of black and white pels and managing each pel individually.

Ocular cryptanalysis is a popular solution for image encoding. Using secret sharing constructs, the encoding process encrypts a secret image into the portions which are noise-like secure images which can be transmitted or distributed over an entrusted communicating channel. Using the belongingss of the HVS to coerce the acknowledgment of a secret message from overlapping portions, the secret image is decrypted without extra calculations and any cognition of cryptanalysis.

Basic ( 2, 2 ) VC strategy

In the ( 2, 2 ) VC strategy each secret image is divided into two portions such that no information can be reconstructed from any individual portion. Each portion is printed in transparences. In Figure 1. the decoding procedure is performed by stacking the two portions and the secret image can be visualized by bare oculus without any complex cryptanalytic calculations.

Figure 1. Construction of ( 2,2 ) VC Scheme

There is a secret image to be shared among n participants. The image is divided into n transparences ( portions ) such that if any m transparences are placed together, the image becomes seeable. If fewer than m transparences are placed together, nil can be seen. Such a strategy is constructed by sing the secret image as a set of black and white pels and managing each pel individually.

Generalization of ( K, n ) VC strategy

Naor-Shamir [ 2 ] generalized their consequences by utilizing the undermentioned theorem/lemma.

Lemma: There is a ( K, K ) strategy with m=2 k-1, I±=2 1-k and

r= ( 2 k-1! ) .

We can build a ( 5, 5 ) sharing, with 16 bomber pels per secret pel and 1 pel contrast, utilizing the substitutions of 16 sharing matrices. In ( K, n ) secret sharing strategy: “ an N-bits secret shared among n participants, utilizing thousand bomber pels per secret spot ( n strings of manganese ) , so that any K can decode the secret ” .

Contrast: There are 500 & lt ; m and 0 & lt ; I± & lt ; 1:

If pixel=1 at least vitamin D of the corresponding m bomber pels are dark ( “ 1 ” ) .

If pixel=0 no more than ( 500 – I±m ) of the m bomber pels are dark

Security: Any subset of less than k portions does non supply

any information about the secret.

Proposed Work:

In ( 2, 2 ) visual cryptanalysis which was implemented Naor & A ; Shamir in [ 6 ] , where the decoded image is twice that of original secret image because the pel P expanded into two bomber pels where the consequence is called pixel enlargement. That affects the contrast of the resulting image. The job for the pixel enlargement and contrast was majorly discussed in literature. The old work on pixel enlargement and contrast optimisation shows that research worker did attempts to cut down the enlargement and optimise the contrast of the secret image. Further they portrait the procedure of making the portions utilizing mathematical representations and chiefly they focus the security and contrast status [ 1 ] .

Pseudo – Randomized Ocular Cryptography Scheme

In ( 2, 2 ) ocular cryptanalysis strategy we have one secret halftone ( grey graduated table ) image ( SI ) as input to the algorithm. Where SI is consider as a matrix Sij where I and J shows pixel places and I, j= 1, 2, 3aˆ¦ n. All stairss of algorithm in this strategy are shown in Figure 2.

Input signal: Secret Gray graduated table image ( SI )

End product: Valid Shares Share1, Share2


Step1: – Pixel Sij with place I and J is the input called original pel.

Step2: – Use pixel reversal i.e SijA? = 255 – Sij.

Step3: – Use pseudo – random figure generator ( 0.1 to 0.9 )

to cut down Si jA? indiscriminately.

Step4: – Take the difference of SijA? with original pel Sij.

Step5: – Use pseudo-random figure generator to cut down

reversed value of SijA? randomly.

Step6: – Use pixel reversal i.e SijA?A? = 255 – SijA? .

Step7: – Shop in matrix as image called portion 1.

Step8: – Take the difference of two random figure generators with original pel Si J.

Step9: – Use pixel reversal i.e SijA?A?A? = 255 – SijA? .

Step10: – Shop SijA?A?A? in matrix as image called portion 2.

Step11: – Repeat point 1 to 10 for all pels from original image ( i.e. matrix of original image )

Figure 2. Psuedo – Randomized Ocular Cryptanalysis

In our strategy the decoded image is same in size of original secret message as there is no pixel enlargement consequence found. However the nature of the algorithm is every bit general as with many other strategies of the decrypted image which is darker and contains a figure of ocular damages. Our decoded secret image is darker than the original. The decoded secret image has increase the spacial declaration nevertheless, largely of ocular cryptanalysis strategy has shown the same consequence in their decoded image [ 6 ] .

After proving many different images from light to dark in declaration it was observed that the proposed algorithm could non take dark true image significantly with high contrast and so bring forth the meaningless portion. Majorly it was found that the portions reveal the information. However on light contrast we have seen that algorithm generates the perfect meaningless portions as shown in Figure 3.

( a )

( B )

( degree Celsius )

( vitamin D )

Figure 3. Pseudo-Randomized ocular Cryptography consequences ( a ) Secret Image ( B ) Share1 ( degree Celsius ) Share 2 ( vitamin D ) Stacking of Share 1 and Share 2


Based on our observation that proposed algorithm could non give perfect meaningless portions in instance of the dark or high contrast secret image, we have added preprocessing elements to alter the dark or high degree of grey image into lighter one ( called preprocessed image or halftone image ) . This is to be done before giving input secret image to proposed algorithm.

We change the pel values to white ( 255 ) on the footing of the place of the pel. We use uneven and even combination of the pel values in the matrix as follows:

Method 1: If i=j=odd and i=j=even

pel ( one, J ) = 255

Method 2: If i=odd & A ; j=even OR i=even & A ; j=odd

pel ( one, J ) = 255

This preprocessing convert the secret image into lighter one in contrast and so give to the proposed algorithm for processing. The consequence of these added elements with both type of preprocessing is shown in Figure 4.

( a )

( B )

( degree Celsius )

( vitamin D )

Figure 4. Improved Pseudo-Randomized ocular Cryptography consequences

( a ) Secret Image ( B ) Share1 ( degree Celsius ) Share 2 ( vitamin D ) Stacking of Share 1 and Share 2

Simulation and Consequences

We have implemented our algorithm in Java engineering. To see the public presentation of our algorithm on different secret images. The simulation consequences are shown in Figure 5. It is shown in Figure 4 that after giving the true grey scale image as secret image has better consequences in comparing of algorithm without preprocessing. Because of secret image ( true grey scale image ) which reveals the secret wholly in portions in instance of without preprocessing. However utilizing preprocessing ( half toning ) the portions shows some information about the secret. This can be bettering to farther by utilizing extended preprocessing on the same processed image. The intent of preprocessing is to fixing the image on a certain degree that the algorithm must non uncover the secret words from the image. Figure 4 shows the perfect meaningless portions and after stacking, we get the secret. The input which is observed as the preprocessed secret image. The consequence from stacking of portion is so perfect in instance of the preprocessing which is shown in the Figure 4.

( a )

( B )

Figure 5. Ocular Cryptography consequences ( a ) Smile ( B ) Monalisa


We have shown that the ( 2, 2 ) imposter – randomized ocular cryptanalysis in pattern where the portions are generated based on pel reversal, random decrease in original pel and minuss of the original pel with old portions pixel. The original secret image is divided in such a manner that after OR operation of qualified portions we reveal the secret image. Our strategy has shown less pixel enlargement which is desirable and good for the concluding retrieval of the secret image. Some contrast is change and damages are still seeable in the consequences of these strategies. However, by spliting the pels into two or more sub pel retrieve the secret image with more damages and bad declarations.

In our strategy the consequences are better than and the size of the retrieve image is the same as the original. However, size of pixel addition provides more relaxation for alliance of the portions. This is still a researchable country to cut down this consequence and besides our proposed strategies have shown high degree of security because of entropy. The future work is to better the contrast and cut down the pel enlargement in the end point secret image. Further extend this work to utilize this technique with colour images and besides see 3D images for making the portions that have partial secret which reveals the secret stacking to each other.


We thank vastly our direction for widening their support in supplying us substructure leting us to use them in the successful completion of our research paper.

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Kylie Garcia

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