REVIEW ON SECRET IMAGE SHARING USING QR CODE GENERATION TECHNIC

Review Article Impact Factor: 4.226 ISSN: 2319-507X
Priyanka A. Singh, IJPRET, 2016; Volume 4 (9): 254-264 IJPRET
Available Online at www.ijpret.com
254
INTERNATIONAL JOURNAL OF PURE AND
APPLIED RESEARCH IN ENGINEERING AND
TECHNOLOGY
A PATH FOR HORIZING YOUR INNOVATIVE WORK
REVIEW ON SECRET IMAGE SHARING USING QR CODE GENERATION TECHNIC.
MISS. PRIYANKA A. SINGH1, PROF. LEENA H. PATIL2, PROF. NAMRATA. S. MAHAKALKAR2
1. M. TECH (CSE), Final Year, PIET Nagpur.
2. Associate Professor, PIET Nagpur
Accepted Date: 15/03/2016; Published Date: 01/05/2016

Abstract: Patent safety and verification have become increasingly more important in regular
life. The digital watermark is one of the method invented to handle this problem. In this
paper, a digitally invisible watermark is inserted in a quick response code image by means of
wavelet transform. In the embedding process, a binary image, logo, is converted into a
corresponding watermark and then inserted into a selected sub band. The experimental
results explained that, for all the cases considered in this paper is stronger to attacks and as
such it can serve as a viable patent safety and verification tool.
Keywords: QR Code, Watermark, Video, Wavelet Transform.
Corresponding Author: MISS. PRIYANKA A. SINGH
Access Online On:
www.ijpret.com
How to Cite This Article:
Priyanka A. Singh, IJPRET, 2016; Volume 4 (9): 254-264PAPER-QR CODE

255
INTRODUCTION
Digital watermark is a motif of bits included into a digital image, audio or video that identifies
the patent and verification information. The aim of watermark method is to inserting the secret
information seamlessly hidden within into original message, which is healthy against attacks. In
recent years, some developers have proposed the adoption of watermark method. The
watermark can also be inserted in the primary spatial domain of the image. In the main
drawback of spatial domain was that it easy to be hacked and assault.
In the proposed method inserted the patent image into the primary image using (N, N) secret
sharing scheme. This technic could resist impurities such as JPEG compression, resize and noise
addition. There are many method to insert the watermark into frequency domain of the
primary image. The techniques operating on a frequency domain use transformations such as
Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet
Transform (DWT). In, a watermark technique of multispectral image is performed in the
wavelet transform. In the proposed a scheme for color images using wavelet transform based
on texture characteristic and secret sharing.
In this paper, we will propose the blind watermarking algorithm by means of two-level discrete
wavelet transform (DWT) embedded in a QR code image. This paper is arranged as follows. A
barcode is an optically machine-readable label that is attached to an item and that records
relevant knowledge. The knowledge encoded by a QR code may be done up of four
standardized types ("modes") of data (numeric, alphanumeric, byte / binary, Kanji) or, through
supported extensions, virtually any type of knowledge. The QR Code system has become
common outside the automotive industry due to its fast readability and larger storage capacity
compared to common UPC barcodes. Applications contain product tracking, item identification,
time tracking, document management, general marketing, and much more.

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Fig. 1. (a) QR code (b) QR code Structure
A quick response code consists of black modules (square dots) arranged in a square grid on a
white background, which can be read by an imaging apparatus, such as a camera or mobile, and
13th International Symposium on Communications and Information Technologies (ISCIT) 791
processed using Reed-Solomon error correction up to that the image can be appropriately
changed. Knowledge is then extracted from the motif present in both horizontal and vertical
components of the image. Fig. 1(a) shown the example of QR code and the structure of QR
code shown in Fig. 1(b)
PROPOSED METHOD
Process of Watermarking
A binary image of Burapha University logo, is a selected as the watermark. The process of
inserting this watermark was performed on a QR code image on its frequency domain. The QR
code image was first decomposed by a two-level two-dimensional wavelet transform as shown
inFig.2. The following watermark extraction, are bided in a sense that it did not want the
primary QR code image in order to get the embedding watermark. There were two steps in our
algorithm: watermark embedding and watermark extraction.

257
Fig.2. 2-Level 2-Dimensional Wavelet Transform
III. The step of embedding process are outlined as follows Step of watermark image with secret
key
JJJ. I The watermark image was produced as a bit sequence of watermark S. The information
and background contains were set to 1 and –1, respectively.
S={si,1<=i<=N}.si ε{-1,1} (1)
where N is the total number of pictures-cells in the watermark image The pseudo-random
sequence (P) whose each digit can take a value either 1 or –1 was randomly generated with a
secret key for embedding and extracting of the watermark.
P={pi,1<=i<=M},pi ε {-1,1} (2)
Step of QR code image
I. The two-level DWT of M x M image ( ) ti was computed for quick response code image.
II. A watermark was then embedded in sub band LH2 orHL2 or HH2. According to the rule
ti = ti + α.pi.si,I = 1,2,3……..N
Where 𝑡𝑖 is input image. ′𝑡𝑖 is output image with watermark. α is a magnitude factor which is a
unchanged determining the watermark power.
III. After that, the inverse DWT (IDWT) was then applied to obtain the watermarked image.
IV. Compute PSNR

258
Fig.3. Watermark embedding process
The watermark extraction algorithm didn’t used the primary QR code image. An assumption of
the original value of the picture-cell is however required. Thus, an assumption of the primary
value of the pixels was performed using noise reduction technique. In this paper, we use an
averaging3×3mask whose elements were fixed to 1/9. The extraction process are outlined as
follows (Fig.4).The predicted image ti could be gained by smoothing the input image ti
* with a
spatial convolution mask. The prediction of the primary value can be defined as:
Where c is the size of the convolution cover. The watermarked image and the predicted image
were DWT converted independently.
II. The estimate of the Si҇ is indicated by the difference between t* and ҇҇ti
δ =t*i-ti҇=α.pi.si (5)
III. The sign of the difference between the assumption and the actual value is the value of the
embedded bit:
Sgn (δi )=pi.si (6)

259
IV. Compute NC
The watermark was then appraise by multiplying pseudorandom number to the embedded
bit. If wrong pseudo random series was to be used, the scheme should not work.
Fig. 4. Watermark Extraction Process
PREVIOUS WORK
Digital Watermarking Techniques
The most important characteristic of any digital watermarking method are strength, sefety,
imperceptibility, complexity, and conformation. Robustness is defined as if the watermark can
be discover after media (normal) procedure such as filtering, lossy compression, color
correction, or geometric modifications. Security means the embedded watermark cannot be
avoid beyond reliable detection by set attacks. Imperceptibility means the watermark is not
seen by the human visual system.
Intricacy is explained as the effort and time required for watermark embedding and obtained.
Finally, conformation is an operation whereby there is private key or public method (Dittmann,
Mukherjee & Steinebach, 2000).Each of these properties must be taken into consideration
when inserting a certain digital watermarking method. The following part describe a few of the
most appearing digital watermarking methods.

260
A. Spatial and Frequency Domain
Spatial and frequency domain watermarking are refer to graphic images and character. Spatial
domain watermarking little change the pixels of one or two randomly refer subsets of an image.
Changes might include flipping the low-order bit of each pixel. However, this methods is not
good when set to normal media procedure such as filtering or lossy compression (Berghel,
1998). Some of its main algorithms are given below:
i). Additive watermarking: The simplest method for embedding the watermark in spatial
domain is to insert pseudo random noise motif to the intensity of image pixels. The noise signal
is popularly integers like (-1, 0, 1) or sometimes floating point numbers.
ii). Least Significant Bit Modification: A digital image version of this analogue image contains
sampled values of the method at discrete place or pixels. These values are said to be the
presentation of the image in the spatial domain or often refer to as the pixel domain. Spatial
embedding include message into image pixels.
iii). Texture mapping coding Technique: This method is refer only in those images which
have some structure part in it. This process hides the watermark in the structure part of the
image. This algorithm is only suitable for those areas with large number of arbitrary structure
images and cannot be done automatically. This method hides data within the stay random
structure patterns of a picture. Carried out where the first one is A and the second is B. Patch A
image data is brightened where as that of patch B is darkened.
iv). Patchwork Algorithm: Patchwork is a data hiding method developed by Bender et al and
published on IBM Systems Journal, 1996[6]. It is depend on a pseudorandom, statistical model.
Patchwork imperceptibly include a watermark with a particular statistic using a Gaussian
distribution. A pseudo randomly selection of two patches is the more useful characteristic of
any digital watermarking methods are strength, security, imperceptibility, complexity, and
conformation. strongness is defined as if the watermark can be discover after media (normal)
procedure such as filtering, lossy compression, color correction, or geometric modifications.
Sefety means the inserted watermark cannot be avoid beyond reliable detection by is carried
out where the first one is A and the second is B. Patch A image data is brightened where as that
of patch B is darkened.
v). Correlation-Based Method: In this methods, a pseudorandom noise (PN) motif says W(x, y)
is added to cover image I(x, y) Iw(x, y) = I(x, y) + k*W(x, y) where K present the gain factor, Iw
present watermarked image ant location x, y and I represent cover image. Here, if we enhanced

261
the gain factor then although it enhanced the strength of watermark but the quality of the
watermarked image will reduced. Frequency domain watermarking method is also called
transform domain. Values of certain frequencies are altered from their original. Typically, these
frequency alterations are done in the lower frequency levels, since alterations at the higher
frequencies are lost during compression. The watermark is applied to the complete image so as
not to be reduced during a cropping process. However, there is a trade-off with the frequency
domain method. Conformation can be hard since this watermark is referred indiscriminately
across the complete image (Berghel, 1998). Some of its important algorithms are given below:
i). Discrete Fourier Transform: Fourier Transform (FT) is an process that convert a continuous
function into its frequency components. The equivalent convert for discrete valued function
requires the Discrete Fourier Transform (DFT). In digital image processing, the even methods
that are un-periodic can be presented as the integral of sine and/or cosine multiplied by a
weighing methods. This weighing function made up the coefficients of the Fourier Transform of
the signal. Fourier Transform allows analysis and processing of the signal in its frequency
domain by means of analyzing and translate these coefficients.
ii). Discrete Cosine Transform: Discrete Cosine Transform is related to DFT in a term that it
convert a time domain signal into its frequency components. The DCT however only refer the
real parts of the DFT coefficients. In terms of characteristic, the DCT has a healthy energy
compaction characteristic and most of the signal data tends to be concentrated in a little low-
frequency components of the DCT. The JPEG compression method utilizes this characteristic to
separate and reduce insignificant high frequency components in images
iii). Discrete Wavelet Transform Wavelet Transform is a modern method frequently used in
digital image processing, compression, watermarking etc. The transforms are depend on little
waves, called wavelet, of varying frequency and limited time. A wavelet series is a
representation of a square-integral function by a certain ortho-normal series produced by a
wavelet. The properties of wavelet should break primary signal into wavelet transform
coefficients which consist the position data. The primary signal can be completely
reconstructed by performing Inverse Wavelet Translation on these coefficients. Watermarking
in the wavelet transform domain is generally a difficulty of embedding watermark in the sub
bands of the cover image.

262
MODULES
1. Generation of QR code of Image
2. 2-Level DWT for creating sub bands
3. Watermark embedding in QR code
4. Generation of QR code with Watermark
1. Generation of QR code of Image
Much has been said about 2D barcodes, and the discussion has focused on the format of the 2D
barcode itself – QR Code, Data Matrix, and so on. But equally important is the format of what
the barcode itself encodes.2D barcodes encode text, generally, but that text can represent
many things. Commonly, 2D barcodes encode text that represents a URL,
like http://google.com/m. This is a special string of text since it is recognizable as a URL by
readers, and therefore can be acted upon: the reader can open the URL in a browser.2D
barcodes can encode many types of actionable text. Text representing contact information,
when recognized, could trigger a prompt to add the contact to an address book. But this only
works when readers understand that text encodes contact information. For this, we need
standards too. There are some standards -- de facto and otherwise -- already in use. This wiki
attempts to catalog some possible standards for encoding various types of information, and
suggest a standard action associated to them. It is not necessarily complete and contributions
are welcome. The ZXing reader library supports all of the format. mentioned in this wiki and a
bit more

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CONCLUSION AND FUTURE SCOPE
In the proposed Digital watermarking method, a binary image is watermarked into a quick
response Code image. The inserting process is in LH, HL and HH sub bands based on wavelet
transform. The algorithm explain that the watermark with an acceptable visual quality can be
get easily. In future we try to find more efficient ways for more series attacks such as stronger
noise, high compression and geometric distortion etc.
In future work we focus on enhanced the proposed method for more inserting capacity and
also for embedding secret data in audio or video file. In future there is a scope to build a better
method for QR Code image depending on the above theoretical knowledge and the current
method available and also reduce the degradation of image quality.
REFERENCE:
1. R. Z. Wang, Y. C. Lan, Y. K. Lee, S. Y. Huang, S. J. Shyu, and T. L. Chia, “Incrementing visual
cryptography using random grids,” Opt. Commun., vol. 283, no. 21, pp. 4242–4249, Nov. 2010.
2. P. L. Chiu and K. H. Lee, “A simulated annealing algorithm for general threshold visual
cryptography schemes,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp. 992–1001, Sep.
2011.
3. I. Kang, G. R. Arce, and H. K. Lee, “Color extended visual cryptography using error diffusion,”
IEEE Trans. Image Process., vol. 20, no. 1, 132–145, Jan. 2011.

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4. M. Kim, D. Li, and S. Hong, A Robust and Invisible Digital Watermarking Algorithm based on
Multiple Transform Method for Image Contents :Proceedings of the World Congress on
Engineering and Computer Science 2013 Vol I WCECS 2013, 23-25 October, 2013, San Francisco,
USA.
5. C. Guo, C. C. Chang, and C. Qin, “A multi-threshold secret image sharing scheme based on
MSP,” Pattern Recognit. Lett., vol. 33, no. 12, pp. 1594–1600, Sep. 2012
6. K. H. Lee and P. L. Chiu, “An extended visual cryptography algorithm for general access
structures,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 1, pp. 219–229, Feb. 2012.
7. Frank Y. Shih: Digital watermarking and steganography: fundamentals and techniques. Taylor
& Francis, Boca Raton, FL, USA, 2008
8. Jun-Chou Chuang, Yu-Chen Hu & Hsien-Ju Ko. A Novel Secret Sharing Technique using QR
Code, International Journal of Image Processing (IJIP), Volume : Issue (5),pp. 468-475, 2010.
9. J. Fridrich, M. Goljan, and D. Soukal, “Perturbed quantization steganog-raphy with wet paper
codes,” in Proc. Workshop Multimedia Sec., Magdeburg, Germany, Sep. 2004, pp. 4–15.
10. Sushma Yalamanchili, M. Kameswara Rao, “Copyright Protection of Gray Scale Images by
Watermarking Technique using (N,N) Secret Sharing Scheme,” Journal of Emerging
Technologies in Web Intelligence, Vol 2, No 2, pp.101-105, May 2010.
11. Y. Rangsanseri, J. Panyaveraporn and P. Thitimajshima, “PCA/Wavelet Based Watermarking
of Multispectral Images,” 2005 International Symposium on Remote Sensing (ISRS2005), Korea,
12-14 Oct. 2005.
12. Nagaraj V. Dharwadkar and B.B.Amberker, “Watermarking Scheme for Color Images using
Wavelet Transform based Texture Properties and Secret Sharing,” International Journal of
Information and Communication Engineering,Vol 6, No 2,pp 94-101, 2010.
13. R. Z. Wang,Y.C.Lan,Y. K. Lee, S. Y. Huang, S. J. Shyu, and T. L. Chia, “Incrementing visual
cryptography using random grids,” Opt. Commun., vol. 283, no. 21, pp. 4242–4249, Nov. 2010.
14. P. L. Chiu and K. H. Lee, “A simulated annealing algorithm for general threshold visual
cryptography schemes,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp. 992–1001, Sep.
2011.
15. Y. Rangsanseri, J. Panyaveraporn and P. Thitimajshima, “PCA/Wavelet Based Watermarking
of Multispectral Images,” 2005 International Symposium on Remote Sensing (ISRS2005), Korea,
12-14 Oct. 2005.
16. G. Ateniese, C. Blundo, A. D. Santis, and D. R. Stinson, “Extended capabilities for visual
cryptography,” Theoretical Comput. Sci., vol. 250, nos. 1–2, pp. 143–161, Jan. 2001.
17. F. Hartung, J.K. Su, and B. Girod, Spread Spectrum Watermarking: Malicious Attacks and
Counter-Attacks, Proc. of SPIE Vol. 3657: Security and Watermarking of Multimedia Contents,
San Jose, CA, USA, Jan. 1999.

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