Hybrid Technique for Copy-Move Forgery Detection Using L*A*B* Color Space

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Hybrid Technique for Copy-Move Forgery Detection Using L*A*B* Color Space Kavya Sharma1, Shweta Meena2, UmeshGhanekar3 1

Dept. of ECE, N.I.T. Kurukshetra, Haryana, India

kavyasharma49@gmail.com, 2mail2shwetameena@nitkkr.ac.in, 2ugnitk@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.

Fig.1. Example of copy-move digital image forgery.

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

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 postprocessing 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.

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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 188

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.

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Int. Journal of Electrical & Electronics Engg.

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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 overlapping blocks of fixed size.

3)

into

4) Apply Lexicographical sorting and match similar pairs of blocks.

5) Matched blocks are mapped to indicate forgery detection. in

above

section

is

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 ZTZ,  is a × diagonal matrix containing square roots of the eigenvalues of ZTZ, called singular values.

 0 (2) 0 0 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 189

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

Extract features from L*a*b* components of each block as well as after applying SVD on each block.

Detailed Procedure The algorithm mentioned implemented as follows:

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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 nonoverlapping, 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 NITTTR, Chandigarh

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Int. Journal of Electrical & Electronics Engg.

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e-ISSN: 1694-2310 | p-ISSN: 1694-2426

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 ( )=



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)

(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.

D=0.945, F=0.004 AWGN (SNR=30dB)

D=0.989, F=0.003 AWGN (SNR=30dB)

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=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

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0.945

0.944

0.956

0.889

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Int. Journal of Electrical & Electronics Engg.

F

0.030

0.030

Vol. 2, Spl. Issue 1 (2015)

0.032

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

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

Fig. 7. F plot for DCT, SVD and proposed method against different AWGNlevels when size of duplicate region is 64 × 64.

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. 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 IIII 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. 4. D plot for DCT, SVD and proposed method against different AWGN levels when size of duplicate region is 64 × 64.

Fig. 9. F plot for DCT, SVD and proposed method against different JPEG compression quality 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.

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.

Fig. 6. D plot for DCT, SVD and proposed method against different JPEG compression quality levels when size of duplicate region is 64 × 64.

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Int. Journal of Electrical & Electronics Engg.

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