Copy-move forgery detection technique based on ...

International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 5, May 2016

Copy-move forgery detection technique based on DWT and Hu Moments Toqeer Mahmood 1, Tabassam Nawaz 2, Mohsin Shah 4, Zakir Khan 4, Rehan Ashraf 1, Hafiz Adnan Habib 3 1 2

Department of Computer Engineering, University of Engineering and Technology Taxila, Pakistan Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan 3 Department of Computer Science, University of Engineering and Technology Taxila, Pakistan 4 Department of Information Technology, Hazara University Mansehra, Pakistan



digital image [8]. The passive forgery detecting techniques do not require any additional information embedded into the image for authentication [9-12]. The benefits of the passive forgery detection techniques attracted the forensic scientists to work in this field.

Abstract — With the rapid development of sophisticated and user friendly image editing tools, forging the contents of digital images have become easier. Copy-move is one the most common way that is being used to forge the content of a digital image. In copy-move forgery, a region is copied and pasted on a nonoverlapping region in the same image. In this study, we proposed a passive technique for image content authentication to determine the CMF. It decomposes an image through discrete wavelet transform and extracts Hu moments as feature vector from a circle block to detect and localize the forged areas. The experimental results demonstrate that the proposed technique precisely detects and localize the multiple copy-move areas even when the post-processing operations are applied to the images. Index Terms—Copy-move, Authentication, Digital Image.

Forgery,

Hu

a

Moments,

I. INTRODUCTION

b

D

to the recent advances in digital imaging technologies, it is easier to capture and preserve any event in the form of a digital image. Today, the digital images are being used in many fields such as digital news website, insurance claims, evidence in the courtrooms, security and surveillance etc. However, in the presence of powerful image editing tools an ordinary user is able to change the contents of a digital image with ease. Thus, the integrity and authenticity of image contents cannot be taken for granted thereby the truthfulness and verification of image contents is becoming more and more important. The present systems for detecting the forgeries in digital images can be grouped into active and passive techniques [1, 2]. Digital watermarking [3, 4] and digital signatures [5, 6] are known as the active techniques. These techniques need some prior data embedded in the digital images at the time of capturing. Therefore, the application of active techniques is limited [7]. On the other hand, passive techniques investigate the features of an image to validate the content integrity of the UE

Figure 1: An example of CMF: a) The original image, and b) The Forged Image A common way to manipulate the contents of authentic images is copy-move forgery (CMF) that is achieved by cloning or removing a part in the same image. Furthermore, the post-processing operations may be applied to the forged images for removing the visual traces of the forgery applied. An example of CMF is presented in Figure 1. In this paper, we investigate this particular artifact by comparing the internal features of the image because the duplicated areas have identical characteristics. For CMF detection, Hu moments are extracted from the blocks of low approximation (LL) sub-band of discrete wavelet transform (DWT). The remaining contents of the paper are organized as

_______________ Toqeer Mahmood is corresponding author and he is a PhD Scholar in Department of computer Engineering, University of Engineering and Technology Taxila-47050, Pakistan. (email: [email protected])

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 5, May 2016

DWT produces a simple hierarchical framework for interpreting the image information. In this study, the low approximation sub-band (LL) is selected for further implementation. The algorithm divides the sub-band (LL) of size 𝑀 × 𝑁 into overlapping circle blocks of size 𝑏 × 𝑏 = 8 × 8 denoted by 𝐵𝑖 , 𝑖 = 1, … , (𝑀 − 𝑏 + 1)(𝑁 − 𝑏 + 1) and Hu moments as feature representation are extracted from all the circle blocks. Further details of the technique are given in the sub-sequent sections. The proposed technique consists of mainly three parts: 1) feature extraction, 2) feature matching, and 3) forgery output.

follows. In Section 2, the related work of the previous copy move forgery detection (CMFD) techniques is given. Experimental results are provided in Section 4, while Section 5 concludes this work. II. RELATED WORK Recently, many CMFD techniques have been proposed for finding the forged contents. Most of the existing techniques follow a common pipeline that consists of three major steps [7,13], : 1) feature extraction: this step extracts an appropriate feature representation for all the blocks of an image, 2) feature matching: this step finds the best match of the image blocks using the features, and 3) post-processing: this step filters all the detected blocks and remove false positive to improve the detection accuracy. The first attempt for copy-move forgery detection (CMFD) regions was studied by Fridrich et al. [14]. The proposed methods utilized discrete cosine transform (DCT) of overlapping blocks. Popescu and Farid [15], investigated CMFD through principal component analysis (PCA) for representing the overlapping image blocks. Bayram et al. [16] suggested the application of scaling and rotation invariant Fourier-Mellin Transform (FMT) in combination with bloom filters on the image blocks for detecting the image forgery. Zhang et al., [1] came up with LOG (Laplace-Gaussian operator) edge detector for forgery detection after applying smoothing filter on the forged images. Huang et al., [9] attempted to improve the technique presented by Fridrich et al., [14] by reducing the feature length and applied a new methods for the comparison of features. Lin et al., [2] proposed a passive duplication detection technique on the basis of content adaptive quantization table estimation. Zimba and Xingming, [17] presented an approach using DWT and PCA aiming to improve the technique presented by Popescu and Farid [15]. Cao et al., [18], utilized the mean of DCT coefficients and a new feature matching technique is suggested. The authors reduced the feature length and the technique is efficient to post-processing operations such as blurring and noise. Lynch et al., [12] suggested to utilize expanding block technique by direct block matching instead of comparing the features of blocks. Li et al., [11] proposed a method using the low approximation obtained through Gaussian pyramid. The rotation invariant LBP (local binary pattern) features are extracted from the circular blocks. Most recently, Ustubioglu et al., [19] applied basic LBP to an image then after applied DCT to each extracted block for the forgery detection. Mahmood et al. [20] presented a method using DCT and KPCA for detecting forgeries. The scheme is robust to various post-processing operation such as blurring, noise and compression.

A. Feature Extraction 1) Hu moments Image moments have extensively been used in image processing and computer vision [23]. The moments of order (𝑝 + 𝑞) for a 2D function is defined as follows: 𝑚𝑝𝑞 = ∬ 𝑥 𝑝 𝑥 𝑞 𝑓(𝑥, 𝑦)𝑑𝑥𝑑𝑦

(1)

where 𝑝, 𝑞 = 0,1,2, … Using (1) for a grayscale image with pixel intensities 𝐼(𝑥, 𝑦), the moments 𝑚𝑖𝑗 for an image are calculated by: 𝑚𝑖𝑗 = ∑ ∑ 𝑥 𝑖 𝑥 𝑗 𝐼(𝑥, 𝑦) 𝑥

(2)

𝑦

The zero-order moment denotes the mass of an image: 𝑚00 = ∬ 𝑓(𝑥, 𝑦) 𝑑𝑥𝑑𝑦

(3)

The first-order moments (𝑚10 𝑚01 ) are usually utilized to determine the centroid of an image by (𝑥𝑐 , 𝑦𝑐 ): 𝑥𝑐 = 𝑚10 /𝑚00 , 𝑦𝑐 = 𝑚01 /𝑚00 . If the origin of the reference frame is moved to the centroid, the centralized moments can be calculated by: 𝑢𝑝𝑞 = ∬(𝑥 − 𝑥𝑐 )𝑖 (𝑦 − 𝑦𝑐 )𝑖 𝑓(𝑥, 𝑦) 𝑑𝑥𝑑𝑦

(4)

where 𝑝, 𝑞 = 0,1,2, … The centralized moments have the properties of translation invariance, when the image is rotated or scaled the centralized moments will not change. Thus, producing the moments with rotation, scaling and translation invariance is very essential for a lot of applications such as pattern recognition, image processing digital watermarking and multimedia searching. For the purpose, the normalized centralized moments are 𝑟 constructed as, 𝑦𝑝𝑞 = 𝑢𝑝𝑞 /𝑢00 , 𝑟 = (𝑝 + 𝑞 + 2)/2, 𝑝 + 𝑞 = 2,3, …, that are invariant against scaling. The second and third order normalized centralized moments are used to construct seven invariant moments [24], that can have invariance against scaling, translation and rotation. The seven moments are defined as:

III. PROPOSED METHOD Different from the present techniques, this methods uses the circle blocks. Firstly, the given image is decomposed through a well know [21], [22] multiresolution Discrete Wavelet Transform (DWT) into sub-bands (LL, LH, HL,HH). The 157

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 5, May 2016

𝑚1 = 𝑦20 + 𝑦02

(5)

2 𝑚2 = (𝑦20 + 𝑦02 )2 + 4𝑦11

(6)

𝑚3 = (𝑦30 − 3𝑦12 )2 + (3𝑦21 − 𝑦03 )2

(7)

𝑚4 = (𝑦30 + 𝑦12 )2 + (𝑦21 + 𝑦03 )2

(8)

matrix 𝑆 in turn reduces the computational time because a feature vector will be compared to only a small number of closer feature vectors to judge the similarity. B. Feature Matching To determine the similarity between the feature vectors the algorithm uses the sorted matrix 𝑆, Each vector in 𝑆 is compared to only 𝑉𝑛 neighboring vectors. The feature matching steps are described as under:

𝑚5 = (𝑦30 − 3𝑦12 )(𝑦30 + 𝑦12 )[(𝑦30 + 𝑦12 )2 − 3(𝑦21 + 𝑦03 )2 ] + (3𝑦21 − 𝑦03 )(𝑦21 + 𝑦03 )[3(𝑦30 + 𝑦12 )2 − (𝑦21 + 𝑦03 )2 ] (9) 𝑚6 = (𝑦20 − 𝑦02 )[(𝑦30 + 𝑦12 )2 − (𝑦21 + 𝑦03 )2 ] + 4𝑦11 (𝑦30 + 𝑦12 )(𝑦21 + 𝑦03 )

Step 1: To hold the condition of non-overlapping, the algorithm first calculated how the two blocks are far away. Let (𝑥𝑖 , 𝑦𝑖 ) and (𝑥𝑗 , 𝑦𝑗 ) be the left corner coordinates of the two blocks represented by 𝑓𝑖 and 𝑓𝑗 respectively. The algorithm calculates the Euclidean distance to verify if the two blocks are candidate for the forgery or not. In this process the algorithm uses a distance threshold 𝐷𝑡 :

(10)

𝑚7 = (3𝑦21 − 𝑦03 )(𝑦30 + 𝑦12 )[(𝑦30 + 𝑦12 )2 − 3(𝑦21 + 𝑦03 )2 ] + (3𝑦12 − 𝑦30 )(𝑦21 + 𝑦03 )[3(𝑦30 + 𝑦12 )2 − (𝑦21 + 𝑦03 )2 ] (11)

2

2

∀√(𝑥𝑖 − 𝑥𝑗 ) + (𝑦𝑖 − 𝑦𝑗 ) ≥ 𝐷𝑡

In this study, we are using the circle blocks. Therefore, the Hu moments are calculated only in the circular region as described in Figure 2. The reason for using the Hu moments is that they effectively works under various post-processing operations such as blurring, noise and compression. Therefore, the Hu moments are the most suitable features for detecting the forged region in digital images. In our implementation, we selected the first four Hu moments as feature vector representation of the image blocks 𝐵𝑖 as 𝑓 = [𝑚1 , 𝑚2 , 𝑚3 , 𝑚4 ]. The main consideration of selecting the first four moment as feature vector is reducing the computational time of the algorithm. Thereafter, we generated a feature matrix denoted by 𝑀 of size (𝑀 − 𝑏 + 1) × (𝑁 − 𝑏 + 1) for all the image blocks.

(13)

Step 2: If (13) in step 1 satisfy then the similarity between the elements of the two feature vectors is measured through the Euclidean distance. In this process the similarity threshold is denoted as 𝑆𝑡 : 4 2

∀ 𝑆𝑖𝑚(𝑓𝑖 , 𝑓𝑗 ) = √∑(𝑚𝑖𝑘 − 𝑚𝑗𝑘 ) ≤ 𝑆𝑡

(14)

𝑘=1

𝑓𝑖 = [𝑚𝑖1 , 𝑚𝑖2 , 𝑚𝑖3 , 𝑚𝑖4 ], 𝑓𝑗 = [𝑚𝑗1 , 𝑚𝑗2 , 𝑚𝑗3 , 𝑚𝑗4 ] C. Forgery Output In this step, the algorithm generates a black map image where all the values of the matrix are 0. However, the algorithm marks 1 for the blocks that are identified as forged. Morphological opening and closing operations are performed on the output map image to fill the holes and remove the isolated regions, then after the final output is generated. IV. EXPERIMENTAL RESULTS This section provides the comprehensive experimental analysis to show effectiveness of the algorithm. All the experiment are performed with a 2.4𝐺𝐻𝑧 Intel 𝐶𝑖5 processor machine and 𝑀𝑎𝑡𝑙𝑎𝑏2015𝑎. We created the forged images by using the Adobe Photoshop, the size of test images used in the experiments is 200 × 200 pixels and 400 × 400 pixels that are collected from the google image search. In the experiments, we set all the parameters as 𝑉𝑛 = 10, 𝐷𝑡 = 30, 𝑆𝑡 = 0.9. The accuracy of forgery detection is measured through precision ‘𝑝’ and recall ‘𝑟’ rate as defined in (15) and (16).

Figure 2: Pattern of circle block

𝑀=

𝑓1 𝑓2 𝑓3 ⋮ ⋮

(12)

[𝑓(𝑀−𝑏+1)×(𝑁−𝑏+1) ] The feature matrix 𝑀 is then sorted lexicographical that makes the identical features closer to each other. The sorted

𝑝 =

158

𝐹𝑜𝑟𝑔𝑒𝑑 𝐴𝑟𝑒𝑎 ∩ 𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐴𝑟𝑒𝑎 𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐴𝑟𝑒𝑎 https://sites.google.com/site/ijcsis/ ISSN 1947-5500

(15)

International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 5, May 2016

𝑟 =

𝐹𝑜𝑟𝑔𝑒𝑑 𝐴𝑟𝑒𝑎 ∩ 𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐴𝑟𝑒𝑎 𝐹𝑜𝑟𝑔𝑒𝑑 𝐴𝑟𝑒𝑎

method with other existing methods of ‘𝑝’ and ‘𝑟’. The proposed technique has higher accuracy in comparison with other methods even when the blurring radius is larger. Table 2 is showing the performance of the algorithm when the forged images has been contaminated with additive white Gaussian noise. The detection results show that the accuracy is higher compared to other methods even when the noise level is 25 db. Table 3 is showing the results when the forged images are compressed with different quality factors. The algorithm leads to higher accuracy ratios compared to the technique presented in [9]. However, the technique presented in [18] does not address compression attack.

(16)

In the first experiment, Figure 3 is showing the results of proposed detection algorithm when the forged areas have regular shapes. The results show that the algorithm detects the forged areas accurately. The second experiment presents the capability of the algorithm when the forged regions have irregular shapes. The detection results are shown in Figure 4 describes that the algorithm is able to detect forged regions even if the regions are irregular shaped. In the third experiment effectiveness of the algorithm is shown by realizing the multiple copy move forgery. Figure 5 portrays that the detection algorithm is able to detect multiple copy move forgery and produces satisfactory level of accuracy.

TABLE 1 PERFORMANCE COMPARISON OF DETECTION UNDER GAUSSIAN BLURRING ATTACK

Blur filter

Proposed Technique

Huang et al. [9]

Cao et al. [18]

𝑝

𝑟

𝑝

𝑟

𝑝

𝑟

 = 3,  = 0.5

0.998

0.047

0.921

0.089

0.959

0.052

 = 3,  = 1

0.994

0.051

0.914

0.091

0.950

0.055

 = 5,  = 0.5

0.992

0.050

0.920

0.054

0.933

0.059

 = 5,  = 1

0.986

0.045

0.913

0.045

0.939

0.062

Figure 3: The detection results of the algorithm with regular shaped forgery

TABLE 2 PERFORMANCE COMPARISON OF DETECTION UNDER ADDITIVE WHITE GAUSSIAN NOISE ATTACK Proposed Huang et al. [9] Cao et al. [18] Technique Noise Levels 𝑝

𝑟

𝑝

𝑟

𝑝

𝑟

25 db

0.961

0.055

0.824

0.091

0.951

0.055

30 db

0.979

0.047

0.910

0.083

0.974

0.048

40 db

0.991

0.032

0.970

0.042

0.99

0.035

Figure 4: The detection results of the algorithm with irregular shaped forgery

TABLE 3 PERFORMANCE COMPARISON OF DETECTION UNDER JPEG COMPRESSION ATTACK

Proposed Technique Quality factors (Q)

Figure 5: The detection results of the algorithm with multiple copy-move forgery

Huang et al. [9]

𝑝

𝑟

𝑝

𝑟

90

0.998

0.079

0.996

0.009

85

0.985

0.012

0.980

0.015

80

0.983

0.026

0.983

0.027

75

0.958

0.039

0.911

0.047

V. CONCLUSION In the last experiment the proposed technique is compared with other techniques to show the effectiveness of the technique under post-processing operations such as blurring, noise and compression. The results with the Gaussian blurring are given in Table 1, where the forged images are blurred by Gaussian filter with the parameters ( = 3,  = 0.5, 1) and ( = 5,  = 0.5, 1). Table 1 is comparing the proposed

We have proposed an automatic and efficient passive copymove forgery detection technique based on DWT and Hu moments where the features are extracted using the circle blocks. It does not require any digital watermark or signature for forgery detection. Experimental results demonstrate that the technique is capable of detecting forged areas precisely 159

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 5, May 2016 [20] T. Mahmood, T. Nawaz, A. Irtaza, R. Ashraf, M. Shah, and M. T. Mahmood, "Copy-move Forgery Detection Technique for Forensic Analysis in Digital Images," Mathematical Problems in Engineering, (Accepted-Online). [21] G. Li, Q. Wu, D. Tu, and S. Sun, "A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD," in Multimedia and Expo, 2007 IEEE International Conference on, 2007, pp. 1750-1753. [22] Y. Hou, C. Zhao, Y. Cheng, and Z. Zhu, "Image Watermarking Resynchronization to Geometric Attacks in DWT Domain," JDCTA, vol. 4, pp. 88-98, 2010. [23] J. Flusser and T. Suk, "Rotation moment invariants for recognition of symmetric objects," IEEE Transactions on Image Processing, vol. 15, pp. 3784-3790, 2006. [24] M.-K. Hu, "Visual pattern recognition by moment invariants," information Theory, IRE Transactions on, vol. 8, pp. 179-187, 1962.

even in the presence of post-processing operations such as blurring, noise and compression. The proposed technique is also compared with other methods. In comparison with other methods the proposed technique has higher accuracy ratios. Thus, we believe our technique can give a little contribution to the area of digital image forensic. REFERENCES [1]

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Toqeer Mahmood is currently serving as Programmer at University of Engineering and Technology Taxila, Pakistan. He completed his MS Computer Engineering in 2010 from Center for Advanced Studies in Engineering (CASE) Islamabad, Pakistan. He is currently pursuing his Ph.D. in Image Forensics from University of Engineering and Technology Taxila, Pakistan. His research interests include Image Processing, Computer Vision, Computer Networks and Numerical Techniques.

Tabassam Nawaz is currently serving as Associate Professor at the Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan. He completed his MS Computer Engineering in 2005 from Center for Advanced Studies in Engineering (CASE) Islamabad, Pakistan and subsequently in 2008, he completed his Ph.D. in Computer Engineering. He has published number of research papers in reputed Journals and Conferences. His areas of interest are Software Engineering, Programming Languages, Data Structure, Computer Graphics, Networks and Digital Image Processing.

Mohsin Shah is currently serving as lecturer at the Department of Information Technology Hazara University Mansehra, Pakistan. He has done his B.Sc. Telecommunication Engineering from University of Engineering and Technology Peshawar, Pakistan in 2007 and M.Sc. Telecommunication Engineering from University of Engineering and Technology Taxila, Pakistan in 2012. He has 2 years of diversified experience in the field of cellular mobile communication systems. His research interests include Image Processing, Optics and Photonics, and Data Security.

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Zakir Khan has completed his Bachelor of Computer Science (BSCS) Master in Computer Science and Masters in Computer Science (MSCS) in 2015 from Hazara University Mansehra, Pakistan. He is currently enrolled for his PhD in Computer Science at the Department of Information Technology Hazara University, Pakistan. He is a skilled programmer and his research interests include Image and Signal Processing, Optical Fiber Communication, Network Security and Computer Networks.

Rehan Ashraf completed his MS Computer Engineering in 2010 from Center for Advanced Studies in Engineering (CASE) Islamabad, Pakistan. He is currently pursuing his Ph.D. in Digital Image Processing from University of Engineering and Technology (UET) Taxila, Pakistan. His areas of interests are Content Base Image Retrieval (CBIR), Digital Image Processing, Computer Networks, Mobile Networks and Numerical Techniques.

Hafiz Adnan Habib completed his MS (Electrical Engineering) in 2004 and PhD (Electrical Engineering) in 2007 from University of Engineering and Technology, Taxila, Pakistan. He is currently serving as Head of Department of Computer Science in UET Taxila Pakisan. His research interests include Speech Processing, Image and Video Processing, Software Development, Artificial Intelligence and Artificial Neural Networks.

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