This document summarizes a research paper that proposes a new algorithm to detect copy-move forgery in digital images using Discrete Wavelet Transform (DWT). The algorithm works by applying DWT to decompose the input image into sub-bands, dividing the low-frequency sub-band into overlapping blocks, sorting the blocks lexicographically, identifying duplicate blocks based on their positions, and calculating shift vectors between matching blocks to detect the duplicated regions. The algorithm is more accurate and efficient than previous methods as it performs detection at the lowest DWT resolution to reduce computational complexity. Experimental results on test images show the algorithm can accurately detect copy-move forgeries with different sized duplicated regions.

1. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 5, July 2012
DWT Based Copy-Move Image Forgery
Detection
Preeti Yadav ,Yogesh Rathore ,Aarti Yadu

II. LITERATURE REVIEW
Abstract— In an age with digital media, it is no longer true that
seeing is believing.In addition, digital forgeries, can be Since the key characteristics of Copy-Move forgery is
indistinguishable from authentic photographs. In a copy-move that the copied part and the pasted part are in the same image,
image forgery, a part of an image is copied and then pasted on a one method to detect this forgery is exhaustive search, but it
different location within the same image .In this paper an is computationally complex and more time is needed for
improved algorithm based on Discrete Wavelet Transform detection. A. C. Popescu and H. Farid proposed a similar
(DWT)is used to detect such cloning forgery. In this technique
detection method [2], in which the image blocks are reduced
DWT (Discrete Wavelet Transform) is applied to the input
image to yield a reduced dimensional representation.After that in dimension by using Principal Component Analysis (PCA).
compressed image is divided into overlapping blocks. These But the efficiency of detection algorithm was not good,
blocks are then sorted and duplicated blocks are identified. Due because, blocks are directly extracted from the original
to DWT usage, detection is first carried out on lowest level image, resulting in a large number of blocks. D. Soukal,
image representation so this Copy-Move detection process proposes DCT based copy-move forgery detection in a single
increases accuracy of detection process. image, In which The image blocks are represented by
quantized DCT (Discrete Cosine Transform) coefficients,
Keywords- Digital Tempering,DWT Copy-Move forgery. and a lexicographic sort is adopted to detect the duplicated
image blocks [3]. B.L.Shivakumar and Dr. S.Santhosh
I INTRODUCTION
Baboo have proposed copy-move forgery detection method
Copy-Move forgery is performed with the intention to based on SURF, which detects duplication region with
make an object ―disappear‖ from the image by covering it with different size. Experimental result shows that the proposed
a small block copied from another part of the same image[1]. method can detect copy-move forgery with minimum false
match for images with high resolution[4] . To increase the
Usually, such an image tampering is done with the aim of speed of operation process many researchers use blocking
either hiding some image details, in which case a background approaches [5]. G.Li, Q.Wu, D.Tu developed a sorted
is duplicated, or adding more details. Whichever the case, neighborhood method based on DWT (Discrete Wavelet
image integrity is lost.Because the copied segments come Transform) and SVD (Singular Value Decomposition) [6].In
from the same image, the color palette, noise components, this method the computation of SVD takes lot of time and it
dynamic range and the other properties will be compatible is computationally complex.
with the rest of the image, thus it is very difficult for a human
eye to detect. Sometimes, even it makes harder for technology III. PROPOSED METHOD
to detect the forgery, if the image is retouched with the tools
In this proposed method an image is scanned from the
that are available.
upper left corner to the lower right corner while sliding a B×B
block. The DWT transform is calculated For each block, the
DWT coefficients are stored as one row in the matrix A. The
matrix will have (M+B+1)(N–B+1) rows and B×B
columns.The rows of A are lexicographically sorted . The
DWT coefficients for each block are now being compared
instead of the pixel representation, if two successive rows of
the sorted matrix A are found, the algorithm stores the
positions of the matching blocks in a separate list B,and
increments a shift-vector counter C. For all normalized shift
Figure 1. Example of Copy-Move forgery (a) original image
vectors, the matching blocks that contributed to that specific
(b) tampered image shift vector are colored with the same color and thus
identified as segments that might have been copied and
moved.
The Proposed method of copy-move forgery detection has
following main parts.
Manuscript received July, 2012
1. Discrete Wavelet Transform
Preeti Yadav:, Final year M-Tech CSE, CSVTU Bhilai,RITEE Raipur, 2. Lexicographic Sorting
(e-mail:preetiyadu@yahoo.co.in).Raipur, India,Mobile-9827974272 3. Shift Vector Calculation
Yogesh Rathore:, :, Department of CSE, CSVTU Bhilai,RITEE Raipur, 4. Neighbor block matching
(e-mail:yogeshrathore23@gmail.com).Raipur, India,le-Mobi9301058533.
Aarti Yadu, Department of IT, CSVTU Bhilai,RITEE Raipur,
(e-mail:artiyadu@gmail.com).Raipur, India. 3.1 Discrete Wavelet Transform
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All Rights Reserved © 2012 IJARCSEE

2. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 5, July 2012
Wavelet decomposition of the images is used due to its s = (s1, s2) = (x1 – y1, x2 –y2).
inherent multiresolution characteristics. The basic idea of
using Discrete Wavelet Transform is to reduce the size of the Because the shift vectors –s and s correspond to the same
image at each level, e.g., a square image of size 2j ×2j pixels at shift, the shift vectors s are normalized[9].
level L reduces to size 2j/2 × 2j/2 pixels at level L+1. At each
level, the image is decomposed into four sub images. The sub The normalized shift vectors s(1),s(2), …, s(K), are those
images are labeled LL,LH, HL and HH, The notation LH, HL whose occurrence exceeds a user-specified threshold T:
and HH correspond to the vertical, horizontal and diagonal C(s(g)) > T for all g = 1, …, K.
components of the image respectively. LL corresponds to the
coarse level coefficients or the approximation image. This 3.4 Neighbor Shift Matching
image(LL) is used for further decomposition.. These sub
For a suspected pair of blocks, the system compares
images can be combined together to restore the previous
features of nearby blocks of both of the blocks of a pair which
image which was decomposed.
are at the same vector distance from the corresponding
Below figure shows the image pyramid[7].Level-0 image is block.Neighbor Shift value is calculated by subtracting two
used for matching of blocks and then these matched blocks equivalent feature vectors. Shift vector of the entire suspected
are carried to the next higher level. Final match is performed duplicate region will be same. Two copied and then moved
on the original image itself. areas will yield some pair of identical features. The same shift
vector will be formed by this. For a particular number of
neighbors this shift vector will be checked . Same shift vector
will be showing the duplicated region.
IV. EXPERIMENTAL RESULT
(a) (b) (c)
Figure 3. Forgery detection result (16*16)(a) original image(b) tampered
image (c) detection result
Figure 2. Image pyramid
3.2 Lexicographic Sort
The lexicographic or lexicographical order, (also known
(a) (b) (c)
as lexical order, dictionary order, alphabetical order or
lexicographic(al) product), is a generalization of the way the
Figure 4 Copy-Move Forgery detection result(48*48) (a) original image(b)
alphabetical order of words is based on the alphabetical order tampered image (c) detection result
of letters.
TABLE I. COMPARISIONWITH DIFFERENT SIZED COPY-MOVE
An important property of the lexicographical order is that it IMAGE REGION
preserves well-orders, that is, if I and J are well-ordered sets, Copy-Move Region Matched
then the product set I ×J with the lexicographical order is also Image Size Region Accuracy
well-ordered[8]. Size No of No of in %
Pixels Pixels
In this step lexicographic sorting is performed on the rows of 256×256 16×16 256 256 100%
matrix A. if two consecutive rows of the sorted matrix A are 256×256 48×48 2304 2208 96%
found, the algorithm stores the positions of the identical
blocks in a separate list B and increments a shift-vector
counter C. V. CONCLUSION
In this paper an algorithm for detecting copy move forgery
3.3 Shift Vector Calculation using Discrete Wavelet Transform (DWT) is proposed,.Our
algorithm has lower computational complexity, since
Let (x1, x2) and (y1, y2) be the positions of the two exhaustive search for identical blocks is performed only on
matching blocks. The shift vector s between the two the image at the lowest resolution. In future , I would like to
matching blocks is calculated as apply Principal Component Analysis, PCA, to the feature
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All Rights Reserved © 2012 IJARCSEE

3. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 5, July 2012
vector to reduce its dimension, so time complexity will be
reduced. The algorithm gave best performance for detection
of small size copy move forgery.
Ms Arti Yadu has done B.E. in Information
REFERENCES Technology from Chhatisgarh Swami Vivekanand University
Bhilai,C.G..Her area of Interest is Artificial Intelligence and Network
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USA: TR2004-515, 2004.
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Cleveland OH, USA, 2003
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Duplication Forgery in Digital Images Using SURF‖ IJCSI International
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for detecting duplicated regions in image forgeries based on DWT and
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[8] Vivek Kumar Singh and R.C. Tripathi‖ Fast and Efficient Region
Duplication Detection in Digital Images Using Sub-Blocking Method
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[9] Rafael C. Gonzalez, Richard E. Woods, Steven L.Eddins,―Digital Image
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AUTHORS PROFILE
Mrs Preeti Yadav is Assistant Professor in
Department of Computer Science and Engineering at MM ollege of
Technology, Raipur, C.G. ,India.she is Pursuing Master's degree(M-Tech 4th
semester) in Computer Science From Chhattisgarh swami vivekanand
University C.G.,India. Currently. Her research interest includes: Image
Processing and Cryptography,Information Systems Security.
Mr. Yogesh Rathore is a Sr. Lecturer in Department of
Computer Science and Engineering, Raipur Institute of Technology, Raipur
(c.g.) . He is M-Tech in Computer Science .His area of interest include
Digital image processing & Computer Graphics.
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