forgery (copy-move) detection in digital images using block method

International Journal of Collaborative Research in Engineering Sciences(2348-9707) Volume I Issue 2, April, 2014

1 ISSN: 2348-9707© IJCRES | ijcres.com

FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHOD

AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR Department of Computer Science and Engineering

Babu Banarasi Das University, Lucknow [email protected], [email protected], [email protected]

ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc. INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.

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1. INTRODUCTION Now a day, it is easy task to create digital image forgeries from the advanced digital cameras. Some of the tools found in the editor are: cropping, resizing an image, selecting part of a (lasso tool), removing rotating objects, unwanted part of an image, merging pictures together. Therefore it’s become a challenging task to verify the originality of an image.

Digital image forgery detection methods are classified into two approaches, which are active and passive approach. In active approaches, the digital images require signatures, watermarking etc. which are pre-processing. Passive approach is different from active approaches; passive approach does not need any watermark embedded in advance.

The copy-move forgery is one of the difficult types of forgery. In this forgery one portion of an image are copied and then select the desired location for pasting in same image. The target of this type of forgery is to hide or add some main feature in same image. A reduced dimension representation is proposed to A.C. Popescu [2], in this image blocks are given by PCA (principal component analysis). After that to get duplicated regions we performed lexicographically sorting into every block. H. Huang [3] first calculates SIFT descriptors of an image, which are not depending on rotation and illumination etc. For detecting copy-move forgery in the image, all these descriptors are matched with each other.

2. PROPOSED METHOD In our proposed method first we take an input image and then check image is in RGB or Gray. If image is not in gray scales, convert the image into gray scale image, then following the step hich are shown in figure-1.

Figure-1 Flow Chart of Proposed Method

Gray Scale Conversion

Block Creation

Feature Extraction

Exact Match

Shift Vector Calculation

Detection Result

Input Image



2.1 Discrete Wavelet Transform

A discrete wavelet transforms (DWT) is wavelet transforms for which the wavelets are discretely sampled. The basic idea of discrete wavelet transform is to reduce at each level for the size of the image.

The haar wavelet transforms is the simplest of the all wavelet transform. In this simply input value 2n, sorting the difference and passing the sum. This process is repeated recursively, pairing up the sums to provide the next scale: finally resulting in 2n-1 differences and one final sum.

Figure 2

Figure 3 Type [2] an example of the 2D DWT

2.2 Block Creation Figure 1 depicts the flow chart of proposed methods. The input image of size x×y is divided into ‘a’ blocks of size b×b pixels by moving the block point to point on the image[1]. Each block of the image is iteratively compared to every other block. The adjacent neighbors of the marked blocks are compared, in copy-move forgery.

2.3 Exact Match

So that, every block contains nine feature vectors V = v1s, v2, v3, v4, v5, v6, v7, v8. We contain an array a, all these block is sorted into it. The starting pixel of this block will contain in array of row and column indices with features of block. It means, this array has contained d [M – b + 1] *[N – b + rows] and [9+2] columns. In sorting, first nine columns may be used. Since, radix sort is sufficient method for the integers of normalization by features [4].

Alphabetical order or lexicographical product is generalization of the way the alphabetical order of words is based on the alphabetical order of their component letters. So that, the highest priority is given by serial, first priority is given to v1 to v5 and left for the other vectors features v6 to v9. One pixel is containing information which is contributing to two different sub blocks. Strongly correlated features are given by it [5].

2.4 Feature Extraction

Formally, the positions are let be consider as [j1, j2] and [k1, k2]. Between the two matching blocks, the shift vector s is calculated as- s = [s1, s2] = [j1 – k1, j2 – k2]. So that, the same shift is corresponding to the shift vector s and –s, the normalization is done for shift vectors s. If necessary, we get s1≥0 by multiplying by -1. So that for every pair of block matching, the normalized shift vector counter C is incremented by one: C (s1, s2) = C (s1, s2) + 1. The sorted matrix A contains the counter C incrementation for every pair of consecutive matching’s rows. Before the algorithm starts, the shift vector is initialized zero. At the end of the matching process, different normalized shift vectors occur by the counter C indicates the frequencies. Then the entire normalized shift vectors s (1), s (2)… s (K) find by the algorithm, whose occurrence exceeds an user-specified threshold T: C(s(r)) > T for all r = 1… K. The matching blocks that contributed to that specific shift vector are colored with the same color and thus identified with segments that might have been moved and copied; it is for all normalized shift vectors [6].

2.5 Detection of Duplicated Region The algorithm identified the size of the smallest segment which is related to the value of the threshold T. The algorithm may miss some not-so-closely matching blocks of large value. While too many false matches may introduce to too small value of T. We repeat that the Q factor Controls the sensitivity of the algorithm to the degree of matching between blocks, while the block size B and threshold T control the minimal size of the segment that can be detected. We perform erosion for removing such false matches followed by dilation. Before one or two erosions, the boundaries are shrunk from erosion of the matched block. Unwanted blocks are removing from it for the small blocks. For getting forged region for the original shape, we performed dilation of same numbers of time [5].

3. Experimental Result In the experiments carried out for the detection process, several images are tempered by copying and pasting one image block over another, in the same image using malicious tools. The whole process was implemented in MATLAB (R2011a) and executed on a computer of CPU 2.0 GHz with secondary storage memory of 320 GB and main memory 3 GB. Experimental data set consists of data



sets, I which has 50 forged images and data set II having 10 noisy images (noise level range from 6% to 25% normal noise) and data set III has 10 images saved with low JPEG levels (level range from 5 to 9), data set IV which has 30 original images with natural duplication in them. All images are compressed using discrete wavelet transforms. Block size is set based on image size. The detected results from tempered images shown in figure.

Figure 4 Original Image

Figure 5 Forged Images

Figure 6 Detection Result









Comparison result of propose approach with other existing methods [1] [5].

Table 1 Comparison

Algorithm False Detection Right Detection

[1] 0.222 0.643

[2] 0.721 0.8771

Proposed Method

0.822 0.9633

CONCLUSION In this paper an algorithm for detecting copy move forgery using wavelet transform is proposed. Our method of stand to in the original images for detecting duplicated region attacked by way of rotation (up to some extent), JPEG compression and Gaussian noise.

REFERENCES [1] Tehseen Shahid, Atif Bin Mansoor,” copy-move forgery detection

algorithm for a digital images and a new accuracy metric”, in international journal of recent trends in engineering, Vol 2, No. 2, November 2009.

[2] A.C. Popescu and H. Farid,” Exposing digital forgeries by detecting duplicated image regions”, Technical Report TR2004-515, Dartmouth College, Aug. 2004.

[3] H. Huang, W. Guo, and Y. Zhang, “Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm,” in Proceedings of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2, 2008, pp. 272-276.

[4] Hwei-Jen Lin, Chun-Wei Wang, Yang-Ta Kao, “Fast Copy-Move Forgery Detection,” WSEAS TRANSACTIONS on SIGNAL PROCESSING, Issue 5, Volume 5, May 2009, pp. 188-197.

[5] Vivek Kumar Singh and R.C. Tripathi,” Fast and Efficient Region Duplication Detection in Digital Images Using Sub-Blocking Methods”, in international journal of advanced science and technology, Vol. 35, October, 2011.

[6] Jessica Fridrich, David Soukal, and Jan Lukáš,” Detection of Copy-Move Forgery in Digital Images”.

[7] Shinfeng D. Lin and Tszan Wu,” An Integrated Technique for Splicing and Copy Move forgery Image Detection”, in 2011 4th International Confress on Image and Signal Processing.

[8] Saiqa Khan, Arun Kulkarni,” Robust Method for Detection of Copy-Move Forgery in Digital Images”, in IEEE 2010.

AUTHORS First Author – Akhilesh Kumar Yadav, M.tech. Babu Banarasi Das University, Lucknow, Uttar Pradesh, India. [email protected] Second Author – Deenbandhu Singh, M.tech. Babu Banarasi Das University, Lucknow, Uttar Pradesh, India [email protected] Third Author – Vivek Kumar, Sr. Lecturer Computer Science Department, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India. [email protected]