Fast Detection of Forgery Image Using Discrete Cosine Transform Four Step Search Algorithm

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Journal of Korea Multimedia Society Vol. 22, No. 5, May 2019(pp. 527-534) https://doi.org/10.9717/kmms.2019.22.5.527 Fast Detection of Forgery Image using Discrete Cosine Transform Four Step Search Algorithm

Yong-Dal Shin†, Yong-Suk Cho††

ABSTRACT

Recently, Photo editing softwares such as digital cameras, Paintshop Pro, and Photoshop digital can create counterfeit images easily. Various techniques for detection of tamper images or forgery images have been proposed in the literature. A form of digital forgery is copy-move image forgery. Copy-move is one of the forgeries and is used wherever you need to cover a part of the image to add or remove information. Copy-move image forgery refers to copying a specific area of an image itself and pasting it into another area of the same image. The purpose of copy-move image forgery detection is to detect the same or very similar region image within the original image. In this paper, we proposed fast detection of forgery image using four step search based on discrete cosine transform and a four step search algorithm using discrete cosine transform (FSSDCT). The computational complexity of our algorithm reduced 34.23 % than conventional DCT three step search algorithm (DCTTSS).

Key words: Duplicated Forgery Image, Discrete Cosine Transform, Four Step Search Algorithm

1. INTRODUCTION and Fig. 1(b) shows an original image and a copy- moved forged image respectively. Digital cameras and smart phones, and digital J. Fridrich [1] proposed an exacting match image processing software such as Photoshop, method for the forged image detection of a copy- Paintshop Pro, you can duplicate digital counterfeit move forged image. J. Fridrich [1] studied detect- images relatively easily [1-14]. ing copy-move forgery image and explaining an A variety of algorithms for detecting duplicated efficient and reliable detection method. This meth- forgery images or tampering images have been od can successfully detect the forged image part proposed in many papers [1-14]. A technique of digital image forgery is copy-move image forgery. Copy-move image forgery is a method of forging an image by copying a specific image area of the image itself and then attaching it to another area of the same image [1-14]. The purpose of copy- move image forgery detection is to detect the same or very similar region image within the original image. (a) Original image (b) Copy-move forgery image An example of copy-move forged image is Fig. 1. Conception of copy-move forged image. showed by Fig. 1 (a) and (b), and each of Fig. 1(a)

※ Corresponding Author : Yong-Suk Cho, Address: (31415) Approval date : Apr. 23, 2019 Younamsan-ro 52-70, Eumbong-Myun, Asan-si, Choong- ††Dept. of IT & Securities, U1 University nam, Korea, TEL : +82-41-536-5734, FAX : +82-41-536- (E-mail : [email protected]) 5739, E-mail : [email protected] ††Dept. of IT & Securities, U1 University Receipt date : Feb. 14, 2019, Revision date : Apr. 16, 2019 528 JOURNAL OF KOREA MULTIMEDIA SOCIETY, VOL. 22, NO. 5, MAY 2019 even if the copied area is enhanced/modified [1]. x Popescu [2] proposed Principal Component C y Analysis (PCA) to reduce the representation of di- mensions in image blocks [9]-[12]. We presented an efficient and robust technology that automati- P cally detects duplicate areas of an image. This I technique works by applying PCA first on a small fixed-size image block to produce a reduced dimension representation. Detection of copy-forgery has become one of the areas studied in blind image Fig. 2. Motion vector x and y of copy-move forgery im- forensics. Christlein etc. [3] proposed that copy- age. move forgery detection methods and processing steps are in various post-processing methods. The From the Fig. 2, C block image is copy image of point of [3] is to evaluate the performance of pre- the original image  ,  is copy-move for- viously proposed feature sets [3]. gery image block. We have an original image  Most of the copy-move forgery image detection by shifting motion vector (), we can get the for- methods are divided by overlapping blocks in the gery image , such that[9] matching search area [1-14]. The N×N image size  =    (1) 2 data is divided into (N-B+1) overlapping blocks The C block and P block of the Fig. 2 show copy having a block size of B×B in order to match of block and pasted block respectively. The Fig. 2 is copy-move forgery image detection [6], [9-14]. copy-move forgery image by shifting motion vec- The exhaustive search method require huge tor x and motion vector y. computational complexity to match forged block in We proposed a fast detection method of forgery the copy-move forgery image detection [1-4], [9- image using four step search algorithm using dis- 14]. However, Shin [11]-[14] proposed low com- crete cosine transform (DCT) (FSSDCT) to reduce plexity algorithms to match forged block. Shin computational complexity. The Koga et al [15] pro- [11-14] proposed a fast detection method of the posed the three step search algorithm (TSS) for copy-move forgery image using DCT and spatial motion estimation in the spatial domain [9-12]. The domain. The proposed algorithm reduced computa- TSS algorithm of video has been widely used for tional complexity, when compared to the conven- motion estimation due to its simplicity and ex- tional various copy-move forgery detection meth- cellent performance ods. We proposed four step search algorithm using In this paper, we proposed a four step search fast DCT (FSSDCT). Our FSSDCT algorithm works detection of forgery image using discrete cosine as follows. Our algorithm obtained DCT coeffi- transform. We proposed a new four step search us- cients of 8×8 pixel block for FSSDCT in the fre- ing discrete cosine transform (FSSDCT). The quency domain. The DCT algorithm [16] converts computational complexity of our algorithm reduced the image/video signal in the spatial domain 6into 34.23% than conventional three step search algo- the image/video signal in the frequency domain rithm using DCT (TSSDCT). [9-12], Most of image/video signal energy lies in the DC and low frequency region [1]; These repre- 2. THE PROPOSED METHOD sent the DCT DC (0) and three (1, 2, 3) coefficients Copy-move forgery image is shown in Fig. 2. of Fig. 3 [14]. The high frequency of the DCT do- Fast Detection of Forgery Image using Discrete Cosine Transform Four Step Search Algorithm 529 main is often a small energy value, and the energy input image by 8x8 pixel blocks [10]. The FSSDCT of the high frequency region can be neglected with algorithm starts with a check point with the center little visible distortion [16]. The DCT transform al- of the DCT coefficient (0,0) in Fig. 3 and measures gorithm is equation (1), also the Inverse DCT algo- the horizontal and vertical lines for nine check rithm is shown in Equation (2) [16]. points with a step size(SS) of SS=4, (4 pixels / 4 lines) in order to find copy-moved forged block image, and four-step search algorithm is perfor-

(2) med around black check point. Find the minimum check point by calculating equation (4) from the Inverse DCT (IDCT) is nine check points. From Fig. 3, the minimum check point is (-4,-4). Step 2 : From Fig. 3, Eight check points are set based on the DCT coefficient position of the check (3) points (-4, -4) found in step 1, and the step size The DCT transform coefficient DC value for u=0, is set to 3, 3 pixel / 3 line in step 2. After perform- v=0, is the average value of the DCT transform ing FSSDCT on the eight check points, find check domain for the spatial domain B × B pixel block, points satisfying minimum of equation (4). From where B is the 8 × 8 pixel block [10]. AC co- step 2, the minimum check point is (-7, -7) in Fig. efficients of the DCT transform domain are all oth- 3. er coefficients except the DC value. Step 3 : From Fig. 3, Eight check points are set The proposed algorithm try to find copy-moved based on the DCT coefficient position of the check motion vectors in the forged image using FSSDCT. points (-7, -7) found in step 2, and the step size The copy-moved motion vectors of forged image is set to 2, 2 pixel / 2 line in step 3. After perform- can be obtained by using FSSDCT, which reduces ing FSSDCT on the eight check points, find check computational complexity. It will be described in points satisfying minimum of equation (4). From detail the proposed method in the following. step 3, the minimum check point is (-9,-9). Step 1: We use the DCT transform of Eq. (1) Step 4 : From Fig. 3, Eight check pointers are to compute the DCT coefficients from the forged set based on the DCT coefficient position of the check points (-9, -9) found in step 3, and the step size is set to 1, 1 pixel / 1 line in step 4. After per- forming FSSDCT on the eight check points, find check points satisfying minimum of equation (4). From step 4, the minimum check point is (-10,-10). The best-match block (-10,-10) in the step 4 is found. The best-match check point of DCT coefficients location is the copy-moved motion vector value (-10,-10). The computational complexity of FSSDCT algorithm needs 33 DCT coefficients check points. In the case of a maximum displacement window : step 1, : step 2, : step 3, : step 4 of 10 i.e. w= -10∼+10, the total number of checking

Fig. 3. Algorithm of the DCTFSS. points required is [9(Step 1) + 8(Step 2) + 8(Step 530 JOURNAL OF KOREA MULTIMEDIA SOCIETY, VOL. 22, NO. 5, MAY 2019

3) +8(Step4)] or 33 for search window 21×21 DCT coefficients. On the other hand, check points of the exhaustive search method need 441 check points to search motion vector of copy-move forgery image. We used block difference (BD) for matching of copy-moved forgery image detection based on FSSDCT. In order to search motion vectors x and y of copy-moved image forgery, we found minimum of block difference (BD) of eq. (4) based on FSSDCT.

BD = min       (4) DI(i+ii, j+jj) is DCT coefficients of block at the position (i, j) of reference image, where, i=0,1,2, 3….N-B+1, j=0,1,2,3….N-B+1, ii, jj=0,1,2,3. DI(i+ii+ x,j+jj+y) are DCT coefficients of block at position Fig. 5. The flowchart of proposed DCTFSS. (i+ii, j+jj) of the matching search image, and the motion vectors x and y of copy-move forgery im- ii and jj in Equation (4) require the 4-computational age obtained by FSSDCT. We used 10 pixels for complexity indicated by 0,1,2,3 in Fig. 4. Hence, the maximum displacement window of FSSDCT. The BD of FSSDCT reduces computational complexity. test image is 8 bits/pixel and 256×256 pixels, and If ( BD == 0) block size is 8×8 pixel block. The FSSDCT is div- Copy-moved image forgery block (5) ided into non-overlapping 21×21 pixel blocks to Else search motion vector x and y of copy-move for- Not copy-moved image forgery block gery image block in the matching search block image. Thus, the computational complexity is re- The proposed method is four step search algo- duced to 4 instead of 64 in an 8×8 pixel block to rithm using DCT. The flowchart of the proposed obtain the motion vector x, y in the FSSDCT. The method FSSDCT is shown Fig. 5. From equation (5), if BD is 0, the copied block is the same as the moved block, so that is the copy moving counterfeit block image [11-14]. If BD is not 0, the copied block image is different from the attached block image, so the block image is not a duplicate move counterfeit block [11-14].

3. EXPERIMENTAL RESULTS

We proposed a fast detection algorithm for copy-moved forgery image using four step search algorithm by discrete cosine transform (FSSDCT). Fig. 4. One DC (0) and three (1,2,3) low frequency coefficients. The proposed FSSDCT method reduces computa- Fast Detection of Forgery Image using Discrete Cosine Transform Four Step Search Algorithm 531

Table 1. Computational complexity of the proposed FSSDCT and conventional methods Algorithms IR BS FD CC(%) Exhaustive Spatial 8×8 64 100.0 Fridrich[1] DCT 8×8 64 100.0 Popescu[2] PCA 8×8 32 50.0 Kahn[7] DWT 8×8 64 23.61 TSSDCT[13] DCT 8×8 4 0.73 FSSDCT DCT 8×8 4 0.48

Table 2. Computational complexity of the proposed FSSDCT and TSSDCT Algorithms IR BS Search Area MSACPN FD CC(%) TSSDCT[13] DCT 8×8 -7∼7 25 × 172 4 100.0 FSSDCT DCT 8×8 -10∼10 33 × 122 4 65.77 tional complexity much more than the conventional The proposed FSSDCT algorithm reduced 99.52 forgery detection algorithms. % of computational complexity than exhaustive The test image for the simulation, Bridge, Car3, search [1]. As shown in Table 1, the proposed and Airplane images are 8 bits and the resolution FSSDCT method reduces the computational com- is 256×256 pixels [17]. The matching search area plexity compared to the conventional method [1], of the test image is divided into 21×21 which does [2],[7], because the FSSDCT method used DC and not overlap from the first position (0,0) of the im- three low frequency DCT coefficients which char- age to the last image position (255,255). BD of acteristic of DCT compression in the frequency copy-moved image forgery detection is Equation domain, and it is a matching search area -10∼+ (4) based on FSSDCT method. 10, non-overlapping 21 × 21 pixel block. In this paper, the computational complexity of The Fig. 6, 7, and 8 showed performance of pro- the FSSDCT algorithm requires four DCT co- posed method. Figures showed original images, efficients for the 21×21 pixel block matching search copy-moved image forgery, detection of copy- per checking point. In order to reduce computa- moved image forgery in the Fig. 6, 7, and 8. From tional complexity of the proposed FSSDCT algo- the Fig. 6(c), left black box is copied, right black rithm, one DC and three low-frequency DCT co- box is moved to image forgery block. From Fig. efficients per checkpoint are sufficient instead of 7(c) and 8(c), right black box is copied, left black 64 checkpoints in an 8×8 pixel block. box is moved to image forgery block. From Fig. Table 1 shows the computational complexity 6, 7, and 8, our algorithm detected above 99% copy- and compares the proposed FSSDCT algorithm moved image forgery. Copy-moved forgery image with the existing methods. The proposed fast detection rates of Bridge, Car3, and Airplane are FSSDCT algorithm detected 99% of copy-move 99.70%, 99.17%, and 99.01%, respectively. Detection forgery images. rate of copy-move forgery image (DR) is expre- From Table 1, IR, BS, MSACPN, FD, CC are ssed by equation (6). image representation, block size of reference re- DR =  (6) gion, matching search area checking points num-  ber, feature dimension, computation complexity where, detected pixel number of copy move image (based on reference[1])method, respectively. (DPN), total pixel number of copy move image (TPN). 532 JOURNAL OF KOREA MULTIMEDIA SOCIETY, VOL. 22, NO. 5, MAY 2019

(a) Original Bridge image (b) Copy-Moved forgery (c) Detection block of Copy-Moved Bridge image forgery Bridge image (Black box) Fig. 6. Result of proposed FSSDCT

(a) Original Car3 image (b) Copy-Moved forgery (c) Detection block of Copy-Moved Car3 image forgery Car3 image (Black box) Fig. 7. Result of proposed FSSDCT.

(a) Original Airplane image (b) Copy-Moved forgery (c) Detection block of Copy-Moved Airplane image forgery Airplane image (Black box) Fig. 8. Result of the proposed FSSDCT.

4. CONCLUSION algorithm using DCT (TSSDCT).

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Yong-Dal Shin Yong-Suk Cho is a professor in department of received the B.S., M.S., and Ph. IT & securities at U1 university, D. degree in the Department of Choongnam Korea. He received Electronic Communication Eng- PH.D. degree from Kyungpook ineering from Hanyang Univer- national university, Daegu sity in 1986, 1988 and 1998, re- Korea, 1994. He research areas spectively. From 1989 to 1996, include multimedia security, he was a researcher at Korea digital watermarking, digital forensics. Telecom. He has been a professor in the Department of IT & Securities at U1 University since 1996. His current research interests include finite field arith- metic, cryptography, and error-control coding.