IT19 Color Quick Response Codes for High Profile Security

Color Quick Response Codes for High Profile
Security Application
Dr. Alex Mathews
Sathyashree. S
Department of Information Technology
College of Applied Sciences - Sohar
Sohar, Sultanate of Oman
[email protected]
Department of Information Technology
College of Applied Sciences - Sohar
Sohar, Sultanate of Oman
[email protected]
Dr. Karthikeyan
Department of Information Technology
College of Applied Sciences – Sohar
Sohar, Sultanate of Oman
[email protected]
Abstract -- In image processing, the security is of vital
importance and a major requirement for many applications
which includes the product identification and authentication. In
this paper, we propose a high level security method by
integrating the holograms with the color QR codes and digital
watermarking in order to provide high intensity of security and
robustness. In addition, a spectral estimation technique is used
for color reconstruction that improves the object image. The
simulation work is done using MATLAB and the effectiveness of
the proposed method is confirmed. The results of the spectral
estimation prove the accuracy.
Keywords— Holograms, Security, CQR codes, Data hiding,
Data Storage.
I. INTRODUCTION
Image processing plays a crucial role in the development of
technologies for dealing with security issues. Some of the
important applications of image processing include computer
vision, remote sensing, feature extraction, face detection,
forecasting, optical character recognition, finger print
detection, microscope imaging, medical image processing and
morphological imaging [9]. The reality of security includes
areas like internal security, business applications, economic
security and defense.
This paper presents the research on a practical security
measure for identification and authentication of product in
image processing. In this paper, three levels of security are
provided. CQR code structure and watermarking is used along
with hologram to improve the level of security. Here the secret
text is first converted into a Colored Quick Response code
(CQR code). It is a two-dimensional structure used to transmit
information. It is intended to be decoded at high speed. It has
superior data density allied with high speed reading. Typically,
a QR Code is composed by black white modules we use of
colored modules in order to increase data density.
Color hologram using more than two wavelengths[2] as
reference beam is generated from this QR code. At the
decoding stage the spectral estimation technique is used for
better color reproducibility. As the number of wavelengths
increases the spectral estimation become more accurate.
This CQR code is watermarked into a color image using a
suitable method which has high capacity. Here Integer Wavelet
Transform (IWT) is used and double key is incorporated so it
has high hiding capacity, high security and good visual quality.
Color hologram is generated from this watermarked color
image to provide good level of security[8] and during the
reproduction side spectral estimation technique is used to
improve the color reproduction.
The rest of the paper is organized as follows. The next
Section describes the theoretical background of the proposed
work. In Section III, the simulation work of color QR codes
and holograms are described. Section IV includes the results.
Finally, Section V concludes the paper.
II. THEORETICAL BACKGROUND
A. CQR codes
The proposed CQR Code is made up of 49x49 modules.
A module is defined as a colored square area that represents
data/redundancy bits (00-red, 01-green, 10-blue, and 11white) or function patterns (black over white background).
The size of each module is nxn pixels, depending on the
desired overall CQR code size. Modules are distributed over
two discriminate regions which are the function patterns and
the encoding region.
The function patterns have exactly the same aspect for all
CQR Codes and are divided into: (a) quite zone (b) finder
patterns and (c) separators. The quite zone is the 4-module
white area that surrounds the code on all four sides.
Finder patterns are the three identical symbols located at
the upper-left, upper-right and lower-left corners of the code
and are used for correct image positioning at the decoder.
Separators are the 1x8 or 8x1-module elements that separate
encoding region and finder patterns. The encoding region
contains information and redundancy bits. Figure 1 illustrates
the above described CQR Code elements. From the 2401
(49x49) modules, 192 are inside the standard region and 2209
are inside the encoding region. Since each module represents 2
bits, we have 4418 bits available, from which 1024 are
information bits and 3392 are redundancy bits.
B. Computer Generated Color Holograms
Computer generated holography[1][7] utilizes the wave
theory of light to represent both the object and reference
waves mathematically. The superposition of these waves at
any point in space is calculated to obtain the interference
pattern required for constructing the hologram [3][4].Based on
Fig. 1. The CQR Code structure. Modules are distributed over function
patterns and encoding region.
is used. This is estimated by use of MMSE method as
𝑟̂ = 𝐶𝑟 𝐻𝑡 (𝐻𝐶𝑟 𝐻𝑡)
−1
𝑔
𝐶𝑟 = 〈(𝑟 − 𝑟̅)(𝑟 − 𝑟̅)𝑡 〉
(1)
(2)
where ^ and t denote estimator and transposition respectively
and Cr is a mxm covariance matrix of the spectral reflectance
of many samples of spectral reflectance distribution.
III. SIMULATION
A. Computer Generated Holograms
The computer generated holograms were generated and
reconstructed using Matlab (version R2013a).The reference
wave is a combination of different wavelengths. The loaded
object image is converted into matrix for further manipulation.
The Fourier Transform operation gives the far field amplitude
calculation given in equation. The addition of the far field
matrices of the object and reference is performed by using
matrix addition. The resulting matrix is the hologram matrix.
The square of the matrix gives the intensity values across the
hologram plane. The reconstruction is done using spectral
estimation [6]. More accurate spectral estimation can improve
the color reproduction since the color reproduction depends on
the continuous spectrum of the object. As the number of
recording wavelengths increases, the more accurate will be the
color reproduction. However, it takes longer when the number
of recording wavelengths is increased because the holograms
are sequentially recorded with each recording wavelength.
the Fraunhofer diffraction formula, the Fourier Transform
operation is utilized to give the far field amplitude calculation
required for calculating the interference pattern.
The spectral estimation technique is introduced to the color
digital holography to improve color reproduction Figure 2
shows the principle of color digital holography using spectral
estimation [2]. First, in this technique, three lasers operating at
different wavelengths corresponding to primary colors, red,
green, and blue (R, G, B) are used for recording of hologram
and three reconstructed images are obtained by each
wavelength in the same way as conventional color digital
holography. Second, spectral reflectance distribution on a
pixel to pixel basis is obtained from reconstructed images by
the spectral estimation method. Finally, the tristimulus values
(x,y,z) of the reconstructed image under the arbitrary
illumination are calculated from estimated spectrum.
Parameters are defined as follows: m is the number of
wavelengths for sample, r is the mx1 vector of the spectral
reflectance of the object, H is the 3xm vector comprising the
spectral power distribution of the illuminant and the spectral
sensitivity of the sensor, and g is the 3x1 vector of the
intensity value. To estimate the spectral reflectance
distribution of the object from the three reconstructed intensity
distributions, Minimum Mean Square Error (MMSE) method
Fig. 2. Schematic diagram of four-wavelength color digital holography.
B. CQR code Simulation
In CQR code each module is represented by one out of five
possible colors taken from the 24-bit RGB color space [6].
Red, green, blue and white colors are chosen because of their
maximum equidistance on the RGB color space. This
facilitates color thresholding on the decoding step. The
positioning of the modules in the encoding region is vertical
bottom-up, from the most right to the most left column, as
shown in Figure 4.
The encoding process is simple and shown in Figure 3.
First, we binarize the information that must be transmitted.
Considering that each group of 16 bits represents one symbol,
the redundancy symbols are generated using the ReedSolomon algorithm and then combined with the original data
symbols. Each group of two bits is mapped into one of the 4
possible colors. Finally, the modules are organized. Figure 5
shows an example of synthesized Colored QR Code. This
example will be used throughout the paper. Once the
information has been properly encoded, the synthesized CQR
Code is printed. In our experiments, we considered the size of
1.3 cmx1.3 cm which represents the print-scanned version of
the reduced CQR code.
Input
Message
Binarize the
string data
Mapping to
color codes
Organize the
modules
CQR
CODE
C. Watermarked Computer generated Holograms
Digital watermarks[5] embedded into the product provide
authenticity to the product. The secret user defined watermark
image is embedded into a cover image. The watermarked
image is converted into a computer generated hologram[7]. A
cover image and a secret watermark are provided to the
program. The cover image and secret watermark are the input
parameters to the program.
The watermarked image is resized to avoid overlapping in
reconstructed image and is converted into a computer
generated hologram. The far field diffraction patterns of the
watermarked image and the reference image are obtained by
using Fourier transform. The addition of these diffraction
patterns generates the computer generated hologram. The
reconstruction of the hologram is done by using Discrete
Fourier Transform (DFT) algorithms. The reconstructed image
contains the watermarked image. The secret watermark is then
extracted from the cropped portion of the reconstructed image.
Decoding of the product identification code from the
holographic barcodes requires a reference from the object
wave, which is called as the difference key. The retrieval of
secret watermark from watermarked computer generated
holograms also requires secret key. This help to increase the
level of security because even if an attacker is able to
reconstruct the hologram, he will not be able to access the
product identification code or the secret watermark. This
provides high profile security to the product.
IV. RESULT
Retrieved
message
Binary to
string
conversion
Convert to
Binary
values
Fig. 3. Encoding and Decoding of CQR Codes.
Scan each
module
Computer generated holograms have been integrated with
concepts of CQR codes and digital watermarking to provide
high profile security to the product. The simulation was done
using Matlab version 2013a. The results obtained during the
simulation of holographic barcodes and watermarked
computer generated holograms are discussed in this section.
Fig. 5. Colored CQR Code.
Fig. 4. Positioning of modules in the encoding region
In the generation of holographic barcodes, the input product
identification code is entered by the user using the Graphical
User Interface (GUI). CQR Code corresponding to input code
generated is shown in Figure 6.The CQR Code, after resizing
and modification, is provided as the data to be hidden the
color image.
Fig. 6. Generated CQR Code.
In watermarked computer generated holograms, a secret
watermark image provided by the manufacturer is used to
watermark a cover image. The watermarked image shown in
Figure 7 is taken as object for the generation of computer
generated holograms. The object image is modified and
resized to avoid overlapping. The diffraction pattern
corresponding to the object image obtained using Fourier
Transform operation. The superimposing of diffraction pattern
of object image and reference image generated the
watermarked computer generated hologram, shown in Figure
8. The object image is reconstructed using spectral estimation
which is shown in Figure 9 and the reconstructed image is
cropped to obtain the watermarked image. The secret
watermark is recovered from this cropped portion and from
that CQR is retrieved.
Fig. 9. Spectral Estimation.
V. CONCLUSION
The image security is a major concern for many application
in the current era. In this paper, we propose a security method
that generates a Color QR code for the encrypted text followed
by the digital holography on the color QR codes to improve the
color reproduction using a spectral estimation technique. In
addition, the resulting object is watermarked to give an image
with high security. The simulation has been carried out in
MATLAB and the experimental results show that the proposed
algorithm is effective and provides high level of security.
The future work includes the optical implementation of the
holographic CQR Code and watermarked computer generated
holograms.
REFERENCES
Fig. 7. Watermarked color image.
Fig. 8. Computer Generated Hologram
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