A NEW DATA TRANSFER MATRIX METHODOLOGY FOR IP PROTECTION SCHEME

Volume 119 No. 15 2018, 135-140 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ http://www.acadpubl.eu/hub/ A NEW DATA TRANSFER MATRIX METHODOLOGY FOR IP PROTECTION SCHEME M.Jagadeeswari, Professor & Head /ECE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu jagadeeswari.m@srec.ac.in C.S.Manikandababu Associate Professor /ECE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu manikandababu.shelvaraju@srec.ac.in K.Dhatchayani PG Scholar/ECE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu dhatchayani.1656001@srec.ac.in Abstract The data matrix technique can be used to secure the data from third parties. Existing technique enables the malicious verifiers to remove the embedded watermark and leaked the information. There are several techniques to conceal information inside cover-image. The spatial domain techniques manipulate the cover-image pixel bit values to embed the secret information. The secret bits are written directly to the cover im-age pixel bytes. Consequently, the spatial domain techniques are simple and easy to implement. This paper proposes new watermarking detection technique based on data matrix to product the data from attackers or third parties. Index Terms Data matrix, Watermarking, Least significant bit. I. INTRODUCTION Data matrix is a two dimensional matrix barcode which encodes text or raw data in a pattern of black and white square modules. It helps the barcode scanner determine cell locations and decodes the symbol, characters, numbers, text and actual bytes of data may be encoded, including Unicode character and photos. The data matrix is one of the smaller and most dependable barcode symbologies compared to other barcode types, data matrix is approximately 30 times smaller than a code 39 barcode representing the same data. The proposed an image processing framework for 2D barcode reading techniques for instance the proposed an image processing framework for 2D barcode reading, which includes four main phases, Region of interest detection, code localization, code segmentation, and decoding. 135

The least significant bit (LSB) is the bit position in a binary integer giving the units value. That is determining whether the number is even or odd. The LSB is sometimes referred to as the right most bit, due to convention in positional notation of writing less significant digits further to the right. It is analogous to the least significant digit of a decimal integer, which is the digit in the ones position. In the public verification process, the prover will provide the sensitive information such as the content and embedded positions of makes to the verifier. Once the sensitive information is given away, malicious attackers can remove marks from the IP and resell it. This is a serious threat to FPGA Ip signature techniques. Furthermore, the FPGA IP is essentially a bit stream file. The embedded watermark or fingerprint in this file could be tempered with and covered more easily compared with ASIC. Public verification is a huge challenge in the field of FPGA IP watermarking, it is also one of the main obstacles to its application. II. RELATED WORKS Ya-Lin Lee and Wen-Hsiang Tsai (2013) proposes A new type of signal-rich-art image for applications of data transfer, called signal-rich-art code image, is proposed. The created code image is visually similar to a pre-selected target image and with a given message embedded, achieving the effect of the so-called signal rich art. With its function similar to that of a QR code, such a type of image is produced by encoding the message into a binary bit stream, representing the bits by binary code patterns of 2 2 blocks, and injecting the patterns into the target image by a novel image-block luminance modulation scheme. Each signal-rich-art code image may be printed or displayed, and then re-captured By a mobile-device camera. Skillful techniques for counting the number of pattern blocks and recognition of code patterns are also proposed for message extraction from the re-captured version of the signal-rich-art code image. Good experimental results and a comparison of them with those of an existing alternative method show the feasibility and superiority of the proposed new data transfer method. Dr. Sanjay Kumar Jena(2013) proposes an image based steganography that Least Significant Bits (LSB) techniques and pseudo random encoding technique on images to enhance the security of the communication. In the LSB approach, the basic idea is to replace the Least Significant Bits (LSB) of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The LSB-based technique is the most challenging one as it is difficult to differentiate between the cover-object and stego object if few LSB bits of the cover object are replaced. In Pseudo- Random technique, a random-key is used as seed for the Pseudo-Random Number Generator is needed in the embedding process. Both the techniques used a stego-key while embedding messages inside the cover image. By using the key, the chance of getting attacked by the attacker is reduced. Champakamala.B.S, Padmini.K, Radhika.D. K (2013) proposes a Steganography wherein encrypted data is hid into the image and then image is transmitted in the network. Read the secret and cover image and convert them into gray scale images, then check the size of the secret image with that of the cover image such that size of the secret image should be less than cover image. Encode the secret image into binary using bit gate command and divide it into RGB parts then substitute MSB bits of secret image into LSB bits of cover image. Hide the password with Stego image and send using GSM modem. III. BLOCK DIAGRAM fig 1 : Block diagram IV. PROPOSED WORK The proposed work containing two main works that are, 1) Data matrix image generation, 2) message extraction. Step 1 The given input information is ASCII value. This ASCII value converted into binary value. Step 2 Select the target image to store the information. Step 3 The image pixel value and the binary value are combined by using LSB technique and formed like a matrix form. Step 4 The binary value and the image pixel value are extracted by inverting the LSB technique. Step 5 Then the binary image will be converted into the ASCII value. Finally the data will be retrieved. Encryption process: Read the secret and cover image and convert them into gray scale images, then check the size of the secret image with that of the cover image such that size of the secret image should be less than cover image. Encode the secret image into binary using bit gate command and divide it into RGB parts then substitute MSB bits of secret image into LSB bits of cover image. Decryption process: The reverse process takes place at the receiving end, Stego image can be decrypted using pass-word. V. DATA MATRIX 136

Data matrix is the most secure technique for data hiding. The given input data is transformed into the bits and stored into the pre selected image by using LSB technique. This method is more secure when compare to the other technique. In Data matrix technique the converted binary image will be store like the matrix form. So the third person doesn t easily extracted the information from the image. value of cover image C(i,j) is equal to the message bit m of secret massage to be embedded, C(i,j) remain unchanged; if not, set the LSB of C(i, j) to m. The message embedding procedure is given below- S(i,j) = C(i,j) - 1, if LSB(C(i,j)) = 1 and m = 0 S(i.j) = C(i,j), if LSB(C(i,j)) = m S(i,j) = C(i,j) + 1, if LSB(C(i,j)) = 0 and m = 1 where LSB(C(i, j)) stands for the LSB of cover image C(i,j) and m is the next message bit to be embedded. S(i,j) is the stego image As we already know each pixel is made up of three bytes consisting of either a 1 or a 0. III. Table 1 An example of code pattern recognition LEAST SIGNIFICANT BIT The least significant bit (in other words, the 8th bit) of some or all of the bytes inside an image is changed to a bit of the secret message. Digital images are mainly of two types (i) 24 bit images and (ii) 8 bit images. In 24 bit images we can embed three bits of information in each pixel, one in each LSB position of the three eight bit values. Increasing or decreasing the value by changing the LSB does not change the appearance of the image; much so the resultant steno image looks almost same as the cover image. In 8 bit Fig 2 : LSB insertion mechanisms Fig 3: LSB extraction mechanisms VI. Simulation and Results images, one bit of information can be hidden. The hidden image is extracted from the steno-image by applying the reverse process. If the LSB of the pixel MATLAB Simulation : MATLAB is a high-performance language for technical computing. Mat lab function is an easy to use, user interface function that guides a user through the process of either en-coding & decoding a message into or from the image respectively. In this work, Mat lab is implemented for processing LSB steganography technique with different frame size 256*256, 128*128, 64*64 and simulation results are shown. There are mainly four steps involved in implementing LSB steganography as shown below. a. Conversion of image to matrix 137

In the conversion process of image to matrix we convert the input cover image into matrix values which is stored in a text file. Firstly an image is read from computer, the original image is in the form of RGB which is converted into grey image. The grey image is resized to a particular size of 256*256. Each image has intensity values for every pixel, here these intensity values are stored into a text file. Fig 4: cover image Fig 6: secret image c. Conversion of matrix to image In this stage intensity values are converted back to image. The image obtained has message embedded into it. The cov-er image and the image obtained here have to be identical. Hence the objective of Steganography is satisfied. d. Extraction process In this process we extract the message which was embedded during embedding process. At first declare a message byte, here the size of the message is 8 bits. Read a pixel from the array starting from address=0.extract the LSB and replace the i th bit in the message byte where i =1 to 8 Address=address=1. When i =8, a byte is extracted. Repeat for extracting next byte. Fig 5 : Matrix image b.embedding process After completion of image to matrix the next step is to embed a message into an image. The image obtained during this process is called as steganoembed image. The message is embedded into the intensity values of image obtained during image to matrix conversion. Fig 7:Extracted image VII. CONCLUSION The Data matrix and LSB scheme is proposed, It will not give any sensitive information. The advantage of using LSB is the data are randomly positioned in the image. So, the third parties or the attackers are not hack any information. And the data matrix form is difficult to retrieve the original 138

information. so, the transformation of data is more secure. Comparing with other techniques use of barcodes and data hiding, data transfer using the proposed data matrix code image has several merits: (1) the image has the visual appearance of any preselected target image(2) the proposed method can endure more distortions in acquired versions of the code image like perspective transformation, noise, screen blurring, etc. The enhanced LSB technique described in this project helps to successfully hide the secret data into the cover object without any distortion. Since LSB doesn t contain any information there is no loss of information and secret image re-covering back become undistorted. It occupied 2596 LUT and the timing constrain is 4.506ns.This is the advantage when compared to the other technique. VIII. ACKNOWLEDGMENT I would like to thank Dr.Jagadheeswari Head of the department/ece and Dr. manikanda Babu, Associate professor/ece for discussion and providing valuable suggestion. I would like to thank the reviewers for their suggestion and commants. IX. REFERENCES [1] B. Davis, Signal rich art: enabling the vision of ubiquitous computing, Proc. SPIE 7880: Media Watermarking, Security, and Forensics III, N. D. Memon, J. Dittmann, A. M. Alattar, and E. J. Delp III, Eds., vol. 788002, Feb. 2011. [2] S. Poslad, Ubiquitous Computing: Smart Devices, Environments and Interactions, John Wiley & Sons, Chichester, UK, 2009. [3] E. Ouaviani, A. Pavan, M. Bottazzi, E. Brunclli, F. Caselli, and M. Guerrero, A common image processing framework for 2D barcode reading, in 7th Int. Conf. on Image Process. and Its Appl., vol. 2, no. 465, pp. 652 655, Jul. 1999. [4] C. Zhang, J. Wang, S. Han, M. Yi and Z. Zhang, Automatic real-time barcode localization in complex scenes, in IEEE Int. Conf. on Image Processing, pp. 497-500, 2006. [5] H. Yang, A. C. Kot, and X. Jiang, Accurate localization of four extreme corners for barcode images captured by mobile phones, Proc. IEEE Int. Conf. on Image Processing, pp. 3897-3900, 2010. [6] H. Yang, A. C. Kot, and X. Jiang, Binarization of low-quality barcode images captured by mobile phones using local window of adaptive location and size, IEEE Trans. Image Processing, vol. 21, no. 1, pp. 418-425, 2012. [7] S.Sivasankari, FPGA Implementation of Invisible Video Watermarking Using DWT Technique, Vol.1, no.1, pp.7-12, 2014. [8] Z. Ni, Y. Q. Shi, N. Ansari and W. Su, Reversible Data Hiding, IEEE Trans. Circuits Syst. & Video Technol., vol. 16, no. 3, pp. 354-362, March 2006. [9] P. Y. Lin, J. S. Lee and C. C. Chang, Protecting the content integrity of digital imagery with fidelity preservation, ACM Trans. Multimedia Computing Communications and Applications, vol. 7, no. 3, 2011. [10] R. L. Lagendijk, G. C. Langelaar, and I. Setyawan, Watermarking digital image and video data, IEEE Signal Proc. Mag., vol. 17, pp. 20-46, Sept. 2000. [11] G. Doërr and J.-L. Dugelay, A guide tour of video watermarking, Signal Processing: Image Commun., vol. 18, no. 4, pp.263-282, 2003. [12] W. N. Lie and L. C. Chang, Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification, IEEE Transactions on Multimedia, vol. 8, no. 1, pp. 46-59, 2006. [13] O. Bulan, G. Sharma, and V. Monga, Orientation modulation for data hiding in clustered-dot halftone prints, IEEE Trans. Image Processing, vol. 19, no. 8, pp. 2070-2084, 2010. [13] O. Bulan, and G. Sharma, High capacity color barcodes: per channel data encoding via orientation modulation in elliptical dot arrays, IEEE Trans. Image Processing, vol. 20, no. 5, pp. 1337-1350, 2011. [14] N. Damera-Venkata, J. Yen, V.Monga, and B. L. Evans, Hardcopy image barcodes via block-error diffusion, IEEE Trans. Image Processing, vol. 14, no. 12, pp.1977-1989, 2005. [15] Y. L. Lee and W. H. Tsai, Signal rich art Image a new tool for automati identification And data capture applications using mobile phones, Proc. IEEE Intl. Conf. Acoustics Speech and Sig. Proc., pp. 1942-1946, 2013 139

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