ROBUST INVISIBLE QR CODE IMAGE WATERMARKING IN DWT …€¦ · ROBUST INVISIBLE QR CODE IMAGE...

International Journal of Electronics and Communication Engineering & Technology (IJECET),

ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 4, Issue 7 (2013) © IAEME

International Conference on Communication Systems (ICCS-2013) October 18-20, 2013

B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India 90

ROBUST INVISIBLE QR CODE IMAGE WATERMARKING IN DWT

DOMAIN

V M Jithin1, K K Gupta2

EEE, BITS, Pilani, India

[email protected], [email protected]

ABSTRACT: Quick Response (QR) code is 2d code and widely used in magazine advertisement,

product packet, museum, and tour tickets. It has high data capacity compare to 1d code. The

invisible QR code is watermarked via popular wavelet transform algorithm in images. The

results show that the proposed method is robust and tested against attacks.

KEYWORDS: QR code, watermarking, Wavelets.

I. INTRODUCTION

In this modern era mobile and wireless technology is taking over all fields of life. They are

more than a communication device; they act as your PC, music player, your bank, your

shopping area and more [1]. In near future the smart glasses like google glass are going to

replace the current hand held devices which make to overlay the physical world with a digital

layer of tags, ads, maps etc [2].

In current smart phone scenario, one of the important technology used to connect the physical

world to internet or digital world using smart phone, is QR codes. They are used mainly for

marketing and commerce but their applications are numerous including virtual marketing,

virtual maps, QRpedia etc. We are making the QR code as invisible watermark in image using

digital watermarking technology. Other alternatives proposed were marking QR code in

invisible ink which is only visible with Ultraviolet [3], QR transparent stickers etc.

The main challenge in making invisible QR code is the detector part. Since the detection is

expected to be done in smart phone which has slower processors, the detection must be very

simple with very little processing required before passing information to QR reader. Here we

implement and simulate the QR encoder and decoder in MATLAB to study the effectiveness of

algorithm. This paper uses watermarking scheme in wavelet domain where multiple copies of

QR code is inserted into low frequency components of host image. Since the data requires no

secrecy compared to existing watermarking schemes, public watermarking technique is used

[4] [5] [6].

INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET )

ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 7 (2013), pp. 190-195 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com

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B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India Page 191

The watermarking is intended to encode secret or copyright information into host digital data

to demonstrate and protect the ownership of products. But in this paper we use this

technology to hide QR code in the background rather than using it to protect the information.

Anyway the same methodologies used in this paper can be used for copyright protection

process. In this paper invisible watermarking of QR code is done in wavelet domain.

The main advantages of invisible QR Code are 1. As opposed to adding yet another element to

the marketing piece or direct mailer, marketers can use existing images within the material to

use as a code; 2. It won’t waste advertisement area with QR.

This paper is organized as follows: QR code is explained in section 2. The implementation is

explained in section 3. The simulation and result are analyzed in section 4 and it followed by

conclusion.

II. QR CODE

QR Code is the trademark for a type of matrix barcode, first designed for the automotive

industry [7]. Two-dimensional bar code technology comparing with the traditional one-

dimensional bar code has the following advantages: 1. higher information density; 2. it can

express Chinese characters, images and even sound; 3.with error correction function. More

recently, the system has become popular outside the industry due to its fast readability and

large storage capacity compared to standard UPC barcodes. The code consists of black modules

(square dots) arranged in a square pattern on a white background. QR Code is capable of

handling all types of data, such as numeric and alphabetic characters, Kanji, Kana, Hiragana,

symbols, binary, and control codes. Up to 7,089 characters can be encoded in one symbol [6].

Data can be restored even if the symbol is partially dirty or damaged. A maximum 30% of

codewords can be restored. Fig. 1 shows the QR code structure.

Fig. 1: QR code structure

III. IMPLEMENTATION

The invisible watermarking of QR code is done in wavelet domain as shown in Fig. 2. Discrete

wavelet transform (DWT) is used due to its spatial resolution: it captures both frequency and

location information (spatial information). The multilevel DWT decomposition is used (4 level

Daubechies-4 wavelet transform). The block diagram of watermarking is shown in Fig. 2.

International Journal of Electronics and Communication Engi

ISSN 0976 – 6464(Print), ISSN 0976

International Conference on Communication Systems (ICCS

B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India

The watermark is embedded into host image in 4th level of wavelet decomposition. As we go

deeper with decomposing low frequency sub image (approximate component) the data will get

more concentrate on approximate component in next levels. That is information in high

frequency components (horizontal, vertical and diagonal) will be very less compared to low

frequency component. So inserting can be done on high frequency component since it won’t

destroy high amount of information. Usually these components will be black with small amount

of white patches which corresponds to high frequency portion (sudden variation in brightness)

of host image. So the location of insertion of watermark should be suitably sele

won’t include sudden variations. In this paper we use multiple watermarking techniques

where three copies of QR code is inserted in all three high frequency sub images after multiple

levels of wavelet decomposition. The location of insertio

subimages since at time of reception it would be easier in slower processors to recover QR

code easily by simple pixel manipulations like addition or correlation. The location is found

using approximate subimage such that i

minimum variation in pixel values.

Another important thing to be noted while insertion is the peak level of pixel value of QR code.

Our empirical studies showed that the insertion with pixel value greater than one by fifteenth

of mean value of approximate subimage give better results while if value exceeds one by

hundredth of maximum pixel value of approximate image make the QR visible after

reconstruction. So trade off done between these values to set the maximum pixel value of

inserted watermark.


6464(Print), ISSN 0976 – 6472(Online), Volume 4, Issue 7 (2013)

International Conference on Communication Systems (ICCS-2013)

of Engineering & Technology (BKBIET), Pilani, India



pproximate component in next levels. That is information in high



h amount of information. Usually these components will be black with small amount


of host image. So the location of insertion of watermark should be suitably sele



levels of wavelet decomposition. The location of insertion is made same for all the three



using approximate subimage such that in particular block selected for insertion is the one with

minimum variation in pixel values.

Fig. 2: watermarking procedure


that the insertion with pixel value greater than one by fifteenth



ade off done between these values to set the maximum pixel value of

Fig. 3: recovery of watermark

neering & Technology (IJECET),

Volume 4, Issue 7 (2013) © IAEME

October 18-20, 2013

Page 192



pproximate component in next levels. That is information in high



h amount of information. Usually these components will be black with small amount


of host image. So the location of insertion of watermark should be suitably selected so that it



n is made same for all the three



n particular block selected for insertion is the one with


that the insertion with pixel value greater than one by fifteenth



ade off done between these values to set the maximum pixel value of




B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India Page 193

At decomposition phase most simple algorithm is used to decrease computational complexity.

We use blind recovery technique which does not require the original image for recovery. We

use QR decoder developed by Z Xing (Zebra barcode decoder an open-source, multi-format

1D/2D barcode image processing library implemented in Java, with ports to other languages

powered by google code project). First we take multilevel DWT and give each high frequency

subimage directly to QR decoder. In the absence of any noise it gives direct output. If QR

decoder fails to decode it directly use group of subimages and use majority check to remove

pixel errors and again give it to QR decoder. Still the QR coder fails which happens in presence

of high noise we use threshold techniques to get a binary image with minimum bit errors

compared with original QR. Our studies show that thresholding at levels between 0.3 and 0.4

gives better results. Those data are provided in simulation and analysis section. The block

diagram for recovery algorithm is shown in Fig. 3.

IV. SIMULATION AND ANALYSIS

The entire simulation is done using Matlab. The algorithm is tried for more than twenty five

images and the study mainly concentrated on 4 test images – Lena, Baboon, Air Plane and

House Boat. The first three are 512x512 image while last one is 960x536 pixels. QR code for

testing is generated using QR code generator by Z. Xing project. While the maily used QR is

33x33 pixel QR (encoded with www.facebook.com). The Peak signal to noise ratio (PSNR) is

measured after embedding invisible QR code. The PSNR is given in Table 1. The qualitative

result is shown in Fig. 4

Test Image Lena Baboon Airplane Houseboat

Size 512x512 512x512 512x512 960x536

PSNR 72.66 73.0749 66.5892 73.1276

Table 1: Output performance is measures using peak signal to noise ratio (PSNR) calculation

Fig. 4: The qualitative results- first image without and next same image with watermark

To find out robustness of proposed watermarking techniques, the efficiency is checked with

various attacks. In our smart phone camera scenario most common attacks on images are noise

due to grain, JPEG compression, cropping or white spaces at outer parts of images and rotation.

We tried to model these attacks and found out robustness against them.


ISSN 0976 – 6464(Print), ISSN 0976



Fig. 5: The number of detection Vs

To analyze noise performance of decoding algorithm we used Lena test image with water mark

as base. The salt and pepper noise is added and plot between PSNR and no of

shown in Fig. 5. The algorithm woks well when PSNR is greater than 28 dB.

Fig.

To find out thresholding level noise is fixed at a given value and no of bit errors are finding

with thresholding level between

pepper noise at 0.05 level is shown in Fig

Fig. 7: Number of detection Vs JPEG compression

Fig. 7 shows watermarked image with varying level of JPEG compression. Since the watermark

is inserted in wavelet domain chance of distortion is more as extend of compression increases.

Simulating Lena image with various levels of JPEG compression shows that faithful detection is

not possible if compression is more than 60%. One of the highest p

watermark image is cropping. The chance of small parts at outer ridge of image be cropped

while taking photo in mobile camera is more. To avoid data loss due to these type of attack a

10% margin is set where watermark is not in

part is got cropped chance of recovery is little due to spatial properties of wavelet transform.





The number of detection Vs PSNR with salt and pepper noise


as base. The salt and pepper noise is added and plot between PSNR and no of

5. The algorithm woks well when PSNR is greater than 28 dB.

Fig. 6: Bit error Vs threshold value


with thresholding level between 0 and 1. The plot for bit error vs. threshold value for salt and

pepper noise at 0.05 level is shown in Fig. 6.

Number of detection Vs JPEG compression

7 shows watermarked image with varying level of JPEG compression. Since the watermark



not possible if compression is more than 60%. One of the highest probable chances of attack on



10% margin is set where watermark is not inserted. Even though for smaller images if large




October 18-20, 2013

Page 194

PSNR with salt and pepper noise


as base. The salt and pepper noise is added and plot between PSNR and no of detection as

5. The algorithm woks well when PSNR is greater than 28 dB.


0 and 1. The plot for bit error vs. threshold value for salt and

7 shows watermarked image with varying level of JPEG compression. Since the watermark



robable chances of attack on



serted. Even though for smaller images if large



ISSN 0976 – 6464(Print), ISSN 0976



V. CONCLUSION AND FUTURE

In this paper we try to model a new scheme to embed QR code in host images so as to

hidden from visual perception. We used multilevel wavelet transform and multiple copies

watermark is inserted for faithful and easy detection. The insertion location and value are

found using algorithm. Also we checked the robustness of technique

results show that this scheme gives better result even in presence of noise (When PSNR greater

than 28dB). It can resist JPEG compression upto 60%. Data can be recovered when image is

cropped along the boundary region. However pe

smartphone environment in this study. We are looking into the implementation of this

algorithm in android and iphone platforms and further modification based on performance.

REFERENCES

[1]Lee Garber, Scanning the Future with New Barcodes, IEEE Computer Magazine, 2011, 20

[2]Chung-Hsin Liu Chia-Hong Chou, Two

Implementation and Performance Analysis, IEEE International Conference, 632

[3]K. Kamijo, N. Kamijo, Zhang Gang, Invisible barcode with optimized error correction, IEEE

International Conference on Image Processing, 2008. ICIP 2008, 2036

[4]P.H.W. Wong, O.C. Au, and Y.M. Yeung, A novel blind multiple watermarking technique for

images, IEEE Trans. Circuits System Video Technology, 13(), 2003, 813

[5]P. Premaratne, C.C. Ko, A novel watermark embedding and detection scheme for images in

DFT domain, Proc. 7th Int. IPA, 2, 1999, 780

[6]C. T. Hsu, and J. L. Wu, Hidden digital wate

Processing, 8(), 1999, 58-68.

[7]QR code website -- http://www.qrcode.com/en/index.html

[8]C. S. Lu, and H. Y. M. Liao, Multipurpose watermarking for image authentication and

protection, IEEE Trans. Image Proces

BIOGRAPHY

Jithin V M was born in

B.Tech degree in Electronics and

Government

pursuing his M.

Rajasthan,India. His current research interests focus on

DSP and Wireless communication

Gupta K K was born in

BITS, Pilani, India in

Processing, DSP and





UTURE WORK

In this paper we try to model a new scheme to embed QR code in host images so as to



found using algorithm. Also we checked the robustness of technique with various attacks. The

that this scheme gives better result even in presence of noise (When PSNR greater


cropped along the boundary region. However performance of this algorithm is not tested in



[1]Lee Garber, Scanning the Future with New Barcodes, IEEE Computer Magazine, 2011, 20

Hong Chou, Two-dimensional bar code mobile commerce

Implementation and Performance Analysis, IEEE International Conference, 632

mijo, N. Kamijo, Zhang Gang, Invisible barcode with optimized error correction, IEEE

International Conference on Image Processing, 2008. ICIP 2008, 2036-2039.


, IEEE Trans. Circuits System Video Technology, 13(), 2003, 813-830.


DFT domain, Proc. 7th Int. IPA, 2, 1999, 780-783.

[6]C. T. Hsu, and J. L. Wu, Hidden digital watermarks in images, IEEE Transactions on Image

http://www.qrcode.com/en/index.html


protection, IEEE Trans. Image Processing, 10(), 2001, 1579-1592.

was born in Payyanur, Kerala, India in 1989. He received the

B.Tech degree in Electronics and Communication Engineering from

College of Engineering Kannur (Kerala), India in 2011. He is

pursuing his M. E in Communication Engineering

India. His current research interests focus on

Wireless communication.

was born in UP, India in 1969. He received t

India in 2008. His current research interests focus on

, DSP and Instrumentation.



October 18-20, 2013

Page 195

In this paper we try to model a new scheme to embed QR code in host images so as to make it



with various attacks. The

that this scheme gives better result even in presence of noise (When PSNR greater


rformance of this algorithm is not tested in



[1]Lee Garber, Scanning the Future with New Barcodes, IEEE Computer Magazine, 2011, 20-21.

dimensional bar code mobile commerce

Implementation and Performance Analysis, IEEE International Conference, 632-635.

mijo, N. Kamijo, Zhang Gang, Invisible barcode with optimized error correction, IEEE

2039.


830.


rmarks in images, IEEE Transactions on Image


, India in 1989. He received the

ommunication Engineering from

), India in 2011. He is

at BITS Pilani,Pilani

India. His current research interests focus on Image Processing,

9. He received the PhD degree from

. His current research interests focus on Image

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