Hybrid Technique for Copy-Move Forgery Detection Using LAB* Color Space

Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 188
Hybrid Technique for Copy-Move Forgery
Detection Using L*A*B* Color Space
Kavya Sharma1
, Shweta Meena2
, UmeshGhanekar3
Dept. of ECE, N.I.T. Kurukshetra, Haryana, India
1
kavyasharma49@gmail.com, 2
mail2shwetameena@nitkkr.ac.in, 2
ugnitk@nitkkr.ac.in
Abstract—Copy-move forgery is applied on an image to hide a
region or an object. Most of the detection techniques either
use transform domain or spatial domain information to detect
the forgery. This paper presents a hybrid method to detect
the forgery making use of both the domains i.e. transform
domain in whichSVD is used to extract the useful
information from image and spatial domain in which L*a*b*
color space is used. Here block based approach and
lexicographical sorting is used to group matching feature
vectors. Obtained experimental results demonstrate that
proposed method efficiently detects copy-move forgery even
when post-processing operations like blurring, noise
contamination, and severe lossy compression are applied.
Keywords—Copy-move forgery; Duplicate region detection;
Singular Value Decomposition; CIEL*a*b* color space.
I. INTRODUCTION
Availability and popularity of digital image tampering
tools is increasing day by day. With the help of these tools
an untrained person can also perform forgery on digital
images. Importance of images on internet, media has also
increased, images are being used in several fields such as
military purposes, medical purposes, journalism etc. Thus
developing methods to check the authenticity and integrity
of images has become important. Methods present to detect
image forgery uses either of the two approaches, Active
approach or passive approach. Methods using active
approach use some information about the original image to
detect forgery e.g. watermarking, digital signature, etc.
whereas methods using passive approach do not need any
prior information about the original image to detect forgery.
Forgeries like copy-move, image splicing, image retouching
etc. can be detected using passive detection methods.
Among many other forgeries, copy-move forgery is the
most popular and commonly used image tampering method
whichis used to create a false image. In this type of
tampering a small part of an image is copied and pasted on
another part within the same image. Fig 1.shows a typical
example of copy-move forgery. The main key to detect
copy-move forgery is that the duplicate regions have similar
features like noise, color, texture etc. as they are from same
image. Thus a copy-move detection method should be able
to detect the presence of duplicate region and precisely
locate them. Several methods have been suggested till now
to detect this forgery. An exhaustive search method was
proposed by J. Fridric [1], but
Fig.1. Example of copy-move digital image forgery.
due to its high complexity it was not suitable for practical
use. J. Fridric [1] also proposed a block based method in
which Discrete Cosine Transform (DCT) was used to
extract feature vector from each block. Popscue[2]
suggested a method using Principal component analysis to
extract unique representation of each block. W. Luo [3]
proposed the use of spatial domain features like average
color of blocks and intensity ratio. Kang [4] suggested
using Singular Value Decomposition (SVD) to represent
features. Mahdian [5] suggested use of blur moment
invariants to form the feature vector. Li [6] proposed using
Discrete Wavelet Transform (DWT) and SVD to extract
block representation. SVD is applied on low frequency sub
band of image obtained after applying DWT. Zhang [7]
proposed a method using DWTin which phase correlation
between non-overlapping sub-images is computed to get the
spatial offset. J. Zhao [8] proposed a hybrid method based
on DCT and SVD which is more robust against post-
processing operations. Another approach was proposed by
Fattah [9] in which approximate DWT coefficients are used
to locate forgery. All the methods discussed so far either
take advantage of frequency domain or spatial domain. To
exploit the advantages of both the domains, this paper
presents a block based method which uses CIE-L*a*b*
color space [10] and singular values obtained by applying
SVD on L*, a*, and b* component of image to detect
forgery. Experimental results demonstrate that the proposed
method is efficient to detect the copy-move forgery and
robust against several post-processing operations. Section II
presentsthe proposed detection approach. Experiments and
simulations are presented and discussed in Section III.
Conclusions are given in Section IV.
II. PROPOSED APPROACH
In copy-move forgery,since the copied region is pasted
in the same image, thus to detect this type of forgery, we
need to detect region in the image with similar properties.
Generally it is unlikely to have large similar regions in
anatural image, thus we need to detect presence of large
similar regions. For this purpose, the image is divided into

189 NITTTR, Chandigarh EDIT-2015
overlapping blocks and unique representation of each block
is generated. These unique representations are matched with
each other to find the duplicated blocks.
Algorithm framework
The proposed algorithm includes five steps, given as
follows:
1) Convert RGB image into L*a*b* color space.
2) Divide L*, a*, and b* components into
overlapping blocks of fixed size.
3) Extract features from L*a*b* components of each
block as well as after applying SVD on each
block.
4) Apply Lexicographical sorting and match similar
pairs of blocks.
5) Matched blocks are mapped to indicate forgery
detection.
Detailed Procedure
The algorithm mentioned in above section is
implemented as follows:
Step1: Pre-processing
Let a suspicious image I of size × is converted
from RGB color space to L*a*b* color space [10]. Where
L* represents lightness, a* and b* represent the color
difference values. Studies indicate L*a*b* system is an
excellent decoupler of intensity and color. Thus we have
used L*a*b* color space for extracting feature vectors
which gives us better detection accuracy than achieved by
working in RGB color space.
Step2: Block tilling
The L*, a*, and b* components IL, Ia, Ib are divided into
overlapping blocks of size × pixels, generating ( −
+ 1)( − + 1) blocks per component. Thus for an
image of size × total number of blocks will be(M −
b + 1)(N − b + 1) × 3. Resulting blocks from L*, a*
and b* components are denoted as Lij, aij and bij
respectively, where i and j denotes starting coordinates of
block’s row and column, respectively.
Step3: Applying SVD and extracting feature vectors
SVD is a matrix decomposition method. Let Z be a
matrix of size × , and with rank r. its SVD is given by
=  (1)
Where, U is a × matrix of orthonormal
eigenvectors of ZZT
, V is a × matrix of orthonormal
eigenvectors of ZT
Z,  is a × diagonal matrix
containing square roots of the eigenvalues of ZT
Z, called
singular values.
 =
 0
0 0
(2)
Where  is a square diagonal matrix in ×
. can be
defined as,  = ( , , ,… … , ), where ( ≥
≥ ≥. . … ≥ ) > 0. Large singular values are less
sensitive to noise, and largest singular value is most stable
against some minor distortions.
In proposed method, SVD is applied on each ×
block of Lij, aij and bij. Six elements of the feature vector are
obtained from resulting blocks. The feature vector Vij
contains 8 elements.
= [ , , , , , , , ] (3)
Where , , , are the first four singular values
obtained after applying SVD on Lij. and are the largest
singular values obtained by applying SVD on aij and bij,
respectively. and are the average value of × block
of aij and bij, respectively.
Step4: Matching of similar block pairs
Feature vector for each block is obtained and a matrix
A, named as feature matrix is created by arranging feature
vectors into it. Matrix A will be of size(M − b + 1)(N −
b + 1) × 8, each row denotingthe feature vector of a block.
Note that ifa block is duplicatedthen its feature vectors
will be identical with the original one, thus we need to find
matching feature vectors. For this purpose,rows of Aare
sorted lexicographically to arrange feature vectors of
matching blocks adjacent to each other. Two blocks will be
considered matched only if they satisfy the following three
conditions:
Condition1. If the difference between two adjacent rows
of lexicographically sorted matrix ( ) is found less than a
fixed threshold ( ).
Condition2. If the Euclidean distance between blocks
satisfying above condition is greater than .
( − ) + ( − ) > (4)
It is assumed that duplicated regions are non-
overlapping, thus the value of threshold is selected to
differentiate between overlapping and non-overlapping
blocks.
Condition3. A matching block pair is considered to be a
part of forged region only if there are many other matching
pairs with similar shift vectors. For this purpose, shift
vector is calculated between blocks satisfying condition 2.
The locations of respective blocks are stored in a separately.
Shift vector Scan be calculated as,
= ( , ) = ( − , − )(5)
Where ( , ), ( , ) are the co-ordinates of top left
corner of the blocks corresponding to adjacent feature
vectors in . As shift vector –S and S corresponds to same
shift, only absolute value is considered. For each matching
block pair, the corresponding shift vector counter (C)is
incrementedby one.
( , ) = ( , ) + 1) (6)
In the beginning of algorithm the value of C is kept
zero, C indicates the frequency of occurrence of different
shift vectors. Then we compare the value of shift vector
counter of all shift vectors with a threshold( ) and
choose shift vectors, , , … … , whose occurrence
exceeds the threshold .
( ) ≥ , for all r=1, 2,…, k(7)
Value of this threshold is related with the size of
smallest region that can be identified by algorithm.
Step5: Output

Duplicated blocks for which the test satisfies all three
conditions are mapped in the image to represent the forgery
detection result.
III. EXPERIMENTAL RESULTS
To evaluate the performance of the proposed method,
several experiments are performed on the test images
collected from two databases [11, 12]. We randomly
selected 50 images from these datasets to generate forged
images by copying a region of 32 × 32 pixels from a
random location and pasting onto a non-overlapping region.
Images with larger size forged region are also created by
copying a square region of 64 × 64 pixels. For each
image,the copied region is pasted on four different relative
locations to generate 400 tampered images. In our
experiment all the parameters are set as: b=8, Tdiff=0.05,
Td=40, Tshift=90. The algorithm has been implemented
using Matlab R2013a.
Detection performance was evaluated by determining,
how correctly it can locate the forged regions. The
quantitative measures used for this purpose are
( ) =


(8)
( ) =


(9)
Where  represents the number of correctly detected
copy-move tampered pixels; represents the number of
actual copy-move tampered pixels; represents the number
of falsely detected copy-move tampered pixels;
and represents the total number of pixels detected as
copy-move tampered.
A. Accuracy Test
To test the effectiveness and accuracy of proposed
algorithm we applied detection algorithm on created dataset
of copy-move forged images. Fig. 2 shows the detection
results,tampered images are shown in top row and detection
results by proposed method are shown in bottom row.
Results shows that our algorithm can localize the forged
regions quite precisely as obtained values of D are generally
greater than 0.95 and values ofF equals to 0.
B. Robustness Test
Generally forgers do different post-processing
operations to make an imperceptible tempered image. There
are various post processing operations e.g. Gaussian
blurring, JPEG compression, AWGN etc.To test the
robustness of proposed algorithm against different post
processing operations, some of the forged images were
applied with Gaussian blurring, AWGN and JPEG
compression with different parameters.Fig.3. shows
detection results of copy-move forgeries with these post
processing operations. Table I, II and III show the
experimental results when AWGN, Gaussian blurring,
andJPEG compression are applied with different parameter
values
D=1, F=0 D=1, F=0
Fig. 2.Detection resultsof copy-move forgeries. Tampered images are
shown in top row, detection results by proposed method are shown in
bottom row. D/F rates are given below respectively.
D=0.878, F=0.003D=0.938, F=0.001
Gaussian blurring Gaussian blurring
(w=3,σ=0.5) (w=3,σ=0.5)
D=0.945, F=0.004 D=0.989, F=0.003
AWGN AWGN
(SNR=30dB) (SNR=30dB)
D=0.899,F=0.011D=0.930, F=.007
JPEG compression JPEG compression
(Q=70) (Q=70)
Fig. 3. Shown are the detection results of copy-move forgeries under
multiple post-processing operations. DAR/FPR rates are given below
respectively.
TABLE I. Detection result when tampered images are distorted by AWGN
SNR=40db SNR=35db SNR=30db SNR=25db
32 × 32
D 0.986 0.985 0.980 0.973
F 0.012 0.020 0.023 0.076
64 × 64
D 0.997 0.995 0.992 0.983
F 0.002 0.003 0.003 0.014
TABLE II. Detection result when tampered images are distorted by
Gaussian blurring
w=3, =0.5 w=3, =1 w=5, =05 w=5, =1
32 × 32
D 0.945 0.944 0.956 0.889

191 NITTTR, Chandigarh EDIT-2015
F 0.030 0.030 0.032 0.033
64 × 64
D 0.967 0.956 0.977 0.902
F 0.013 0.015 0.012 0.015
TABLE III. Detection result when tampered images are distorted by JPEG
compression
Q=90 Q=80 Q=70
32 × 32
D 0.973 0.890 0.789
F 0.013 0.038 0.074
64 × 64
D 0.982 0.956 0.910
F 0.003 0.005 0.019
Fig. 4. D plot for DCT, SVD and proposed method against different
AWGN levels when size of duplicate region is 64 × 64.
Fig. 5. D plot for DCT, SVD and proposed method against
differentGaussian blurring(w=5) when size of duplicate region is 64 × 64.
Fig. 6. D plot for DCT, SVD and proposed method against different JPEG
compression quality levels when size of duplicate region is 64 × 64.
Fig. 7. F plot for DCT, SVD and proposed method against different
AWGNlevels when size of duplicate region is 64 × 64.
Fig. 8. F plot for DCT, SVD and proposed method against different
Gaussian blurring (w=5) when size of duplicate region is 64 × 64.
on tampered images respectively. From Fig.3 and Table I-
III it is evident that our method performs accurately in
presence of post-processing operations. To evaluate the
performance of the proposed method the experiments were
performed on other two well-known existing methods, DCT
[1] and SVD [4].
Fig. 9. F plot for DCT, SVD and proposed method against different JPEG
compression quality levels when size of duplicate region is 64 × 64.
Comparison results of performance in terms of average D/F
are shown in Fig. 4-9. From the graph it can be easily
inferred that the proposed method out performs the several
other methods in terms of accuracy and false detection rate
in presence of AWGN, Gaussian blurring, and JPEG
compression.
IV. CONCLUSION
In this paper a hybrid method has been proposed for
copy-move image forgery detection, making use of spatial
and transform domain. The L*a*b* color space is used to
extract color information and then SVDis applied on
L*a*b* components to get transform domain features. The
experimental results show that the proposed algorithm can
efficiently detect copy-move forgery and precisely locate
the duplicated regions. It also exhibits high robustness to
post processing operations like Gaussian blurring, AWGN,
and JPEG compression.Extensive simulation results exhibit
that the proposed method significantly outperforms many
other well-known techniques.
REFERENCES

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[8] J. Zhao and J. Guo, “Passive forensics for copy-move image forgery
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[9] S. A. Fattah, M. M. I. Ullah, M. Ahmed, I. Ahmmed, and C. Shahnaz,
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[11] The USC-SIPI Image Database: http://sipi.usc.edu/database/.
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Hybrid Technique for Copy-Move Forgery Detection Using L*A*B* Color Space

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