International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
A Novel Approach for Forgery Detection of
Images
Vimal Raj V 1, Lija Thomas 2
1 Department of ECE, Christ Knowledge City,
Mannoor, Kerala, India.
2 Department of ECE, ILM College of Engineering & Technology,
perumbavoor, Kerala. India.
Abstrac
Each image has its own unique characteristics or properties. When we edit an image we are changing these characteristics and by
examining the changes in the characteristics we can determine whether the image is edited or not. Image editing can be of two
types, usual editing and copy move forgery. In copy-move forgery a part of original digital image is copied and pasted to another
part in the same original image to make it, forged one. Because the copied part comes from the same image, its important
properties will be compatible with the rest of the image and thus will be more difficult to distinguish and detect these parts.
Keywords- Copy-Move forgery; digital signature; DWT; phase correlation
1. INTRODUCTION
In the past few years, there has been a growing interest in the development of detection of editing in images. For editing
our photos digitally, there are numbers of different photo editing software and tools available. Image editing is a
technique to improve look and feel of photographs and you can compose two or more different photographs or graphics to
make something more appealing, interesting and unique concept.
Each image has its own unique characteristics or properties. When we edit an image we are changing these
characteristics and by examining the changes in the characteristics we can determine whether the image is edited or not.
But the difficult part in image editing is the copy-move forgery. Forgery is the process of making, adapting, or imitating
objects, statistics, or documents with the intent to deceive. As result of powerful image processing tools, digital image
forgeries have already become a serious social problem.
It easy to manipulate digital images and to create forgeries that is difficult to distinguish from authentic photographs. In
earlier days we used watermarking and digital signature methods for image manipulation and forging. But both these
methods requiring the preprocessing of the data such as embedding watermark in the images. This makes them relatively
difficult to apply on images.
Figure 1 Tampered image
Copy-move forgery is a type of image forgery, in which a part of original digital image is copied and pasted to another
part in the same original image to make it, forged one. This can be done for two purposes either to conceal an important
object or sometimes to show more than one object.
Figure 2 Forged test image “Jeep” (above) and its original version (below).
Volume 2, Issue 8, August 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
Examples of the Copy-Move forgery are given in Figures 1-2. In Figure1, an example of copy-move forgery can be seen;
where the original image has one barrel in the ground whereas in forged one, Cloning tool of Photoshop has been used to
show that there are two barrels in the ground.
In Figure 2, you can see a less obvious forgery in which a truck was covered with a portion of the foliage left of the truck
(compare the forged image with its original.
If we take the photo of a room that image have its own characteristics. If we do any usual editing like contrast
manipulation, brightness enhancement etc., we are changing the characteristics of the image by providing some external
characteristics. Here we are actually adding some gradient values to the pixel values of the image that can be detected
using the DWT method. But in the case of copy-move forgery because the copied part comes from the same image, its
important properties will be compatible with the rest of the image and thus will be more difficult to distinguish and detect
these parts.
We reduce the dimension of the forged image by using discrete wavelet transform (DWT). Instead of discrete wavelet
transform we can use singular value decomposition (SVD).But the computation of SVD takes a lot of time and it is
computationally complex. Then the compressed image is split in to overlapping blocks. These blocks are sorted and phase
correlation is used as the similarity criterion for checking the duplicated blocks. The DWT approach drastically reduces
the time needed for the detection process and improves the accuracy of detection.
2. PROPOSED ALGORITHM
First we take an unedited test image. We can read the image by using suitable Matlab code. Then we compress the image
using Discrete Wavelet transform. This DWT compressed image is divided into 8&7 blocks. Then each block is compared
with all other blocks in the image. The comparison can be done based on two criteria.
First we compare each block with every other block in the image based on the intensity values. Thus for that particular
image we get a range of intensity values. We repeat this process for a set of test images and find the average range of
pixel intensity values and from that limit we find the minimum and maximum values (b,c).
Figure 3 Flow diagram of editing detection
Secondly we can compare each block of the image with every other block in that particular image based on the correlation
value. For that particular image we can find the maximum of the correlation value. Similarly we do the correlation
analysis for a set of test images and find the maximum value of correlation for each of these images and from these
maximum values we take the higher value as the correlation threshold.
2.1 Usual Edition Detection
If we do any usual editing like contrast manipulation, brightness enhancement etc., we are changing the characteristics of
the image by providing some external characteristics. Here we are actually adding some gradient values to the pixel
values of the image. To detect the usual edition we go for the block wise comparison based on the intensity values. If any
external characteristics are added to the pixel values of the images, then the range of pixel values will not be within the
range shown by the rsmp code ie, the value will exceed the range b&c.
2.2 Copy Move Forgery Detection
In the case of copy-move forgery because the copied part comes from the same image, its important properties will be
compatible with the rest of the image so when we do the pixel intensity value comparison, its range will be within the
rsmp code range and we cannot detect. So we go for the correlation analysis. Since here the same part are copy pasted, the
correlation value should be above the correlation threshold(a).Thus we can detect the copy pasted part.
2.3 Discrete Wavelet Transform
The basic idea of using Discrete Wavelet Transform is to reduce the size of the image at each level. An example image
along with its wavelet transform applied up to level 3 is shown in Fig. 4.
Volume 2, Issue 8, August 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
Figure 4 An image and its Wavelet Transform
At each level the image is decomposed into four sub images. The sub images are labeled LL, LH, HL and HH. LL
corresponds to the coarse level coefficients or the approximation image.
Figure 5 Image pyramid
If the number of levels used for decomposition is ‘L’, then the matching is performed on the LL image at level ‘L’
denoted by LLL. Fig.5 shows the image pyramid.
2.4 Phase Correlation
This is a method of image registration. The ratio R between two images ‘img1’ and ‘img2’ is calculated as follows:
R= [F (img1) ×conj (F (img2))]/ [||F (img1) ×conj (F (img2)) ||]
The inverse Fourier transform of ‘R’ is the phase correlation ρ.
2.5 RSMP code
Second order derivative filter is used to find out the rsmp code.Then we do the radon transform of the result of derivative
and find its auto covariance . To compute the radon transformation, pixels are divided into four sub pixels and each sub
pixel projected separately.The radon transformation is computed at angles from 0 to 179, in 1 increments.
Find the maximum value of the magnitudes of the FFT of obtained sequences. In the case of rotation, peaks appearing in
the spectrum can help us to determine the angle of rotation transformation. In scaling, there is a direct relation between
the normalized position of peaks fn, and the scaling factor, N:
3. EXPERIMENTAL RESULTS AND EVALUATION
We have compressed images using DWT as compression method and used phase correlation as similarity checking
criterion. We have used block size based on image size and this block size will be doubled as we move to the next higher
level.
Volume 2, Issue 8, August 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
Figure 6 Forgery detection result (a) original image (b) tampered image(c) detection result on LLL level image (d) detection result on
LLL-1 level image (e) detection result on LLL-2 level image (f) detection result on tampered image.
The detected results over tampered image for all DWT levels are shown in Figure 7. To see how these methods perform
under the modifications, we have used US currency note image to illustrate detection as shown in Fig.7
Figure 7 Forgery detection result (a) original image (b) tampered image(c) detection result with 15% normal noise(d) detection result
with 25% normal noise(e) detection result with 35% normal noise (f) detection result with 45% normal noise.
The Fig.8 shows the performance of the algorithm results for the image having more than one duplicated regions.
Figure 8 Forgery detection result (a) original image (b) tampered image having more than one duplicated result.
4. CONCLUSION
When we edit an image we are changing the unique characteristics of the image and by examining the changes in these
characteristics we can determine whether the image is edited or not. In copy move forgery detection part of original
digital image is and pasted to another part in the same original image to make it, forged one. In addition to these copy
paste operation the pixel values can also be changed due to stenographic operation. Here we developed a method based on
DWT for detecting editing operations performed on the image. The DWT based approach drastically reduces the time
needed for the detection process and increases accuracy of detection process.
Acknowledgement
The authors thank the Management and the Principal of Christ Knowledge City, Mannoor, Kerala and ILM College of
Engineering & Technology, Perumbavoor, Kerala for providing excellent computing facilities and encouragement.
References
[1.] Saiqa Khan, Arun Kulkarni, “Reduced Time Complexity for Detection of Copy-Move Forgery Using Discrete
Wavelet Transform”International Journal of Computer Science Applications, Vol. 6, No. 7, pp: 31-36, Sep 2010.
[2.] G.Li, Q.Wu, D.Tu, and Shaojie Sun, “A sorted neighborhood approach for detecting duplicated regions in image
forgeries based on DWT and SVD,” IEEE International Conference on Multimedia & Expo, 2007
[3.] Saiqa Khan, Arun Kulkarni,”Robust Method for Detectoim of Copy-Move Frogery in Digital Images” International
Journal of Computer Science and Engineering, 2010 IEEE
[4.] Myna.A.N. , M.G.Venkateshmurthy , C.G.Patil “Detection ofRegion Duplication Forgery In Digital Images Using
Wavelets and Log-polar Mapping”, in Proc. of International Conference on Computational Intelligence and
Multimedia Applications, Volume 3,13-15,pp.371–377,July2-6,200.
Volume 2, Issue 8, August 2013
Page 58
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
AUTHORS
Vimal Raj V received the B-Tech degree in Electronics and Communication Engineering from Mahatma Gandhi
University, Kerala, India in 2010. He received M.E degree in VLSI Design from Anna University TamilNadu, India, in
2013. He is currently working as an Assistant Professor in Christ Knowledge City Mannoor, Kerala, India. His areas of
interests are Low Power VLSI Design, Digital Circuits, Electronic Circuits, Digital Electronics and Digital System Design.
Lija Thomas received the B-Tech degree in Electronics and Communication Engineering from Mahatma Gandhi
University, Kerala, India in 2010. She received M.Tech degree in Communication Engineering from Mahatma Gandhi
University, Kerala, India in 2012. She is currently working as an Assistant Professor in ILM College of Engineering &
Technology, Perumbavoor, kerala, India. Her areas of interests are Electronics Circuits, Communication Engineering and
digital Image Processing.
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