digital wavelet transform based image forgery ...

SDPS-2015 Printed in the United States of America, November 2015 2015 Society for Design and Process Science

DIGITAL WAVELET TRANSFORM BASED IMAGE FORGERY DETECTION USING POST-PROCESSING Praveen K. Chiluka1, Sunil R. Das1, 2, Mansour H. Assaf3, Satyendra N. Biswas4, Scott Morton1, Emil M. Petriu2, and Voicu Groza2 1

Department of Computer Science, College of Arts and Sciences, Troy University, Montgomery, AL 36103, USA 2 School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada 3 School of Engineering and Physics, University of the South Pacific (USP), Suva19128, Fiji 4 Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh

ABSTRACT The digital format images can be readily altered using various editing tools, without leaving any alteration traces. The duplication of one or more regions of an image is the usual tampering technique where portions from one segment of an image are copied and pasted to other portions of the same image. In this paper, we propose an image forgery detection method based on the digital wavelet transform concept that is capable of identifying forgeries in images. Here, an image is first partitioned into blocks of a uniquely defined size, which are then subjected to the discrete wavelet transform. The discriminative attributes are first removed from the wavelet coefficients of the blocks; the attribute vectors of the blocks are then sorted and finally, the block matching step is applied to identify the copied blocks. The simulation experience demonstrates that the proposed wavelet based approach is highly promising from the viewpoint of effectively identifying forgeries in digital images. The methodology works better when the pictures are highly distorted with distinctive post-transformation, motion blurring, Gaussian noise and intensity variations.

enhance the information content. The subtractive approach, on the other hand, clips part of the information from the original image. Here, the classification of forgery is strictly for the purpose of theoretical studies of the problem and hence, many researchers have addressed the issue of image manipulation in their works without any reference to classification. In a classical example of forgery involving subtractive approach, as shown in Fig. 1, a picture originally showing Joseph Stalin and Nikolai Yezhov together was forged and Nikolai Yezhov was completely erased from the

INTRODUCTION Fig. 1 (a) Original image (left) and (b) forged image (right)

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digital images in binary format could be readily manipulated by forgery that might convey misleading information without any trace of real evidence. A number of available software is specifically designed to aid in one of the conventional approaches to forgery, viz. copy-move-rotate (CMR). The term forgery is conventionally defined as the production of fraudulent copy or imitation of a document, signature, banknote or a work of art. In the realm of digital images, the forgery is the state of the art defined by models based either on an additive or a subtractive approach, contingent upon the content of the original image. The additive approach copies a segment from a random image (or of the same image) and thereafter mingles it with the original image to

picture (Math & Tripathi, 2011). For this particular image, the requirement of rotation does not appear valuable; yet, the artists with little computational knowledge introduced rotation in an image making it difficult for a naked eye to identify the source of forgery. Figure 2 depicts the role of rotation factor in enhancing the precision of forgery. Here, the jet was tilted that convinced a false sense of originality of the image (Ryu et al., 2010). There are several issues involved in forgery detection. The various recognition systems help bring tampering pieces of information from places such as color filter array, source light direction, sensor noise, JPEG blocking, camera reaction and so on. Therefore, large portions of them are not suitable for detecting duplication regions. The intent is to have the

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Fig. 2 Forgery with copy and rotation approach

Fig. 3 Visible watermarking

copied region from the same picture, which may have identical qualities with the original picture. However, these procedures simply check the authenticity of a picture; they do not have the capability of naming the altered regions. Another issue in this context is the multi-layered computational nature of the detection methods. Since, we do not have the least idea about the size, area and state of the duplicated regions, there are practically boundless numbers of methodologies to determining the picture into self-confident areas and the computational issue might make the detection rather difficult. The post-processing like blurring and JPEG compression will be the final problem, which may hide the altering imitations. The proposed wavelet based detection framework discussed in this work is to address all of the aforesaid cases. The presented forgery detection scheme is capable of identifying post-processing issues like motion blurring, noise and intensity variations, Gaussian noise and so on. In what follows, we briefly provide an overview of the various forgery detection methods for digital images.

image and forgery detection might be classified into five categories, viz. methods based on pixel, format, physical parameters, camera or geometry. Figure 4 below provides a flowchart for classification of various image forgery detection techniques. Forgery Detection of Digital Image

Active Methods

Passive Methods

Digital Image Watermarking

Digital Signature

Based on Pixel

Based on Format

Based on Physical Parameters

IMAGE FORGERY DETECTION—AN OVERVIEW Based on Camera

The field of digital image forgery basically evolved with the discovery of the digital photography. Simultaneously, the digital image watermarking was proposed to authenticate digital images. The digital watermarking is a strategy which permits a person to incorporate concealed copyright notices or other check messages into digital images, video or audio signals and archives. This type of concealed message comprises a collection of binary bits depicting information identifying the signal or creator of that signal (which generally may be a name, a place and so forth). The method coins its name from watermarking that is done on paper or cash as a security measure. Though this signal or pattern is available in every unchanged copy of the original image, the digital watermarking might likewise serve as a computerized signature for the picture, video and audio copies. An example of visible watermarking is illustrated in Fig. 3. It shows the embedding of a watermark into the host image. The final image is a watermarked image which contains a visible outline of the watermark. This strategy is very valuable when showing a logo for an organization or to indicate the title of an image. Any legal apparatus intended for authentication of an

Based on Geometry

Fig. 4 Image forgery detection techniques

The basic presumption of the pixel based methods is that any type of manipulation, if connected legitimately, even if not visually distinguishable, yet modifies particular details at the pixel level. For instance, measurable regularities in natural images that stay free of the image content are misused to verify images (Bayram et al., 2006). Contingent upon occurrence of the forgery, the pixel level correspondences might be examined. The cloning (or copy and paste) is most likely one of the least demanding types of forgeries, typically performed so as to cover an object with different parts of the image itself (Bayram et al., 2008). Despite the fact that it is visually challenging to be revealed, such types of forgery could be distinguished by searching for factual comparable parts within the image contents. The format based methods are suitable for revealing measurable connections presented by compression techniques. The JPEG compression is well-known to be a lossy method; some data bits are lost during the procedure.

Specifically, a quantization step involving the discrete cosine transform (DCT) coefficients is chiefly the reason of such misfortune. The full quantization is focused around a table of 192 values, connected with every frequency on an 8 × 8 block basis and may change relying upon the quality setting. It has been shown in Fan & de Queiroz (2003) that such a table could be assessed and concentrated from the substance. The irregularities of everything over the image or feature could be taken as proof of forgery (Chen & Shi, 2008; Kornblum, 2008). In addition, it was demonstrated that in JPEG compression the information is compressed twice and still some particular artifacts are present in an image (Farid, 2009; Lukas & Fridrich, 2003; Qu et al., 2008; Wang & Farid, 2009). Recently, JPEG2000 traces have been contemplated furthermore as verification of adjustment for double compression (Qadir et al., 2010; Zhang et al., 2008). A recent research in compositing recognition considers irregularities in lighting. It has been brought up even with the more modern altering instruments; it is hard to match the lighting impacts on each piece of the composite. This way, contrasts in lighting over the whole image might be taken as proof of forgery. Spearheading commitments in this field have been proposed by Johnson and Farid (2006), where 2-dimensional (2-D) surfaces normal are evaluated to investigate light bearings of distinctive objects in the scene, whose consistency might be utilized as evidence of compositing. The camera based systems of forgery detection are centered on the dismemberment of traces left by the distinctive periods of the imaging system. These artifacts are innate to camera assembling methods and irregularities could be taken as confirmation of forgery. The chromatic distortion appears in an artifact because of a spatial movement in the area where the light with diverse wavelengths hits the sensor. In Johnson & Farid (2006), neighborhood deviation is assessed and its irregularities regarding the global image are taken as a proof of forgery. A majority of the advanced cameras are furnished with round lens which can cause outspread distortions on images. At the point where two images are joined together, it is unrealistic to match such bending among spliced parts and these irregularities may be taken as proof of forgery (Chennamma et al., 2010). The advanced cameras besides capture color with a color filter array (CFA) and this is accomplished by a solitary sensor. For every pixel, one color sample is recorded and the missing ones are acquired by insertion. As an outcome, the particular associations are presented, which are unrealistic to survive when forgery happens (Popescu & Farid, 2004). Ordinarily, images may experience an assortment of post-processing and recompression, which may hinder the viability of customary strategies for forgery identification. Rather than statistical systems, geometric based measurable methods have been proposed, which explain estimations of objects in the earth and their positions in respect to the camera dissecting the projection geometry. Their huge playing point over frameworks focused on low

level picture element is that showing and approximation of geometry is quite less touchy to determination and compression that can exist without much of a perplexity in the measurable examination of pictures and videos. A few systems have been discussed in the literature, each one describing the standards of image structuring and projective geometry (Johnson & Farid, 2006). Yadav et al. (2012) proposed an improved technique based on the DWT, since the DWT approach gives significant results for perceiving the cloning forgery. They detail about the application of the DWT on the input image to yield a diminished dimensional representation. Then, the compressed image is separated into blocks. These blocks are then arranged and fake blocks are recognized. Because of the DWT use, the location is initially completed on the least level image representations; so, this copy-move detection methodology builds accuracy of the recognition procedure. The digital images are now everywhere: on the pages of the magazines, in everyday newspapers, in courts and all over the web. Murali et al. (2012) suggested procedures to perceive such stunning pictures and proceeded to recognize the forged region, given simply the produced picture. This is planned for the system running irregular abnormalities to identify the forgery regions. Qazi et al. (2013) discussed in a survey paper about the visually impaired methods which have been put forward for uncovering imitations. They described the recognition framework for three of the most well-known forgery types, viz. copy-move, splicing and retouching. The image forgery alludes to copying and pasting the content from one image into an alternate image. This strategy is very basic nowadays. This is carried out to conceal the ingenuity of the image. Kaur et al. (2014) showed encouraging plans for location of the parts in an image forgery. A promising blind approach to copy-move image forgery detection using the DCT and dyadic wavelet transform (DyWT) was discussed in (Muhammad et al., 2014). The images of different sizes with different compression ratios and rotations were used in the paper for experimentation in order to evaluate the method’s efficiency. A new algorithm based on the DWT and DCT quantization coefficients decomposition (DCT-QCD) to detect cloning forgery was presented by Ghorbani et al. (2011). The technique is capable of detecting image manipulations as long as the copied regions are not rotated. A series of algorithms including combination of speededup robust feature (SURF) and DWT were proposed in (Hashmi et al., 2014). These algorithms are applied on the entire image rather than on the image blocks and show improvements in terms of computational complexity and invariance to scale and rotation. Besides those as presented above, there exist many other strategies of image forgery detection in the research literature, which, however, could not be discussed here due to space constraint. We now discuss the methodology and implementation details of our proposed approach in the section that follows.

METHODOLOGY AND IMPLEMENTATION The image forgery detection technique is concerned with the suspicion that the copied regions of an image must have a computable resemblance, while the other regions do not. Accordingly, authentic features can be eliminated from regions for duplication recognition. We used the DWT coefficients to extract the energy signatures. The procedure is implemented in a number of stages as indicated in Fig. 5. Assuming that the first pictures are color pictures, the pictures have got to be converted to their grayscale equivalents by computing the weighted normal of the red (R), green (G) and blue (B) segments. The transformation is carried out using the following equation:

in the system. The wavelets are made from an extension of a modified capability called mother wavelet. Here, we provide some reasons for the preference of the DWT for image watermarking as opposed to other transformation methods. Primarily, the DWT is basically an arrangement of filters. Specifically, it incorporates the following two filters: wavelet filter and scaling filter. The wavelet filter is a high-pass filter while the scaling filter is a low-pass filter. The DWT has many variations, viz. Daubechies wavelet, Haar wavelet and so on. We have utilized Daubechies wavelet in our work here. Figure 6 shows the capacity of the DWT for measurement reduction.

where R, G and B are the approximations of the channels named as red, green and blue, respectively, in original color picture, estimated separately. If the first picture is compacted (viz. a JPEG picture), then the decompression will be carried out first before changing over to the color.

RGB to Gray Conversion

Post-Processing (Motion Blurring/Gaussian Noise/Intensity Variation)

Convert Image into Overlapping Blocks

Dimension Reduction using the DWT (Feature Extraction)

Sorting of Elements

Block Matching

Reconstruct Image (Filtering)

Detected Image (Output)

Fig. 5 Proposed algorithm

A square block having B × B pixels is utilized to move the picture from the upper left corner to the lower right corner. The slide step is one and only one pixel and no cushioning will be connected. Hence, the original picture with size of M × N will produce (M–B + 1) × (N–B + 1) covering blocks altogether. The DWT is a logical tool for dynamic disintegration of a picture. It is useful for changing non-stationary signals. The transform is centered on wavelets of variable frequency and time duration. The transform gives both frequency and spatial delineation of a picture. Unlike Fourier transform, the temporary information here is held

LH-1

HL-1

HH-1

Image

f(R, G, B) = 0.2989 x R + 0.5870 x G + 0.1140 x B

Test Image

LL-1

First-Level

Fig. 6 DWT for dimension reduction

As a result of application of the first-level DWT on a picture, we get the estimates: sub-band low-low (LL), flat sub-band low-high (LH), vertical sub-band high-low (HL) and diagonal sub-band high-high (HH) for each block, with n features extracted. After feature extraction, a total of (M + B–1) x (N + B–1) feature vectors are created. We analyze the greater part of the vectors, two vectors at a time, to discover the comparative ones. A practical arrangement is to sort the vectors first and after that, just contrast a vector with its nearby neighbors. On the assumption that the copied locales would have comparative features, the likelihood that their comparing feature vectors are spotted in the sorted vectors is high. In our approach, the lexicographic sorting is used. Each of the feature vectors will then be contrasted until a vector's first feature becomes fundamentally distinctive with that of the current vector. The two vectors will be contrasted within the matching process to find the similarities of the comparing blocks. We expect that if the two vectors A and B are comparable, every component Ai, i = 1, 2, …, n where n is the total number of extracted features in A ought to be like the comparing element Bi in B. The correlation of Ai and Bi is focused on: • •

off chance that Ai x Bi = 0, t = Ai–Bi has to be less than a threshold Ti else, r = Ai–Bi /min (Ai, Bi) must be short of a threshold Ri

All the components in A and B fulfill these two standards and hence, the two comparing blocks of A and B are thought to be comparative. The edge values Ti and Ri for each feature are registered from a training set. The first picture has substantial smooth regions; copied blocks can be found in these locales. We have to evacuate these false positives.

Additionally, if the recognized copied regions are excessively small, they are not captivating and must be separated out as false positives. In our first attempt to filtering, we have to process the movement vector between two blocks:

as an output image. A step by step example illustrating the image forgery detection process is depicted below. Step 1: Importing the original image (Fig. 8) and converting it from color to grayscale.

s = (dx, dy) = (xi–xj, yi–yj) (xi, yi) and (xj, yj) being the higher left corner pixels of the primary and secondary blocks, respectively. Next, the movement vector s is standardized: if dx < 0, s = –s else, if dx = 0 and dy < 0, s = –s The lengths of the movement vectors are next determined. All the movement vectors whose lengths are shorter than the threshold Nd will be evacuated. This step guarantees that the copied areas do not originate from the same smooth locale. The greater part of the remaining block combines are then assembled. Finally, if a collection contains less than some predefined number of matching blocks Nt, the whole assemblage will be evacuated, ensuring that the extent of a copied region is sensible. As a result of filtering, all the copied blocks are eventually found. An image is then constructed based on the majority of the copied regions. A more detailed discussion based on simulation results is presented next.

Fig. 8 Original image

Step 2: The image was then forged as shown in Fig. 9.

SIMULATION EXPERMENTS In this section, we present simulation results with implementation details of the proposed approach using MATLAB 2010a. Figure 7 below illustrates fetching of the required files and images in MATLAB. The size of images being used in this simulation are 256 x 256 pixels. The colored original images are converted to grayscale in order to apply forgery detection.

Fig. 9 Forged image

Step 3: Copy-move regions have been identified and displayed with two black spots using normal detection method (Fig. 10).

Fig. 7 Importing files and images in MATLAB

Initially, the forgery detection method is applied. A window pops up with the given input image which undergoes block extraction, finding of the duplicate regions and checking the Euclidean distance and finally, depending on the system properties and efficiency, another window pops up with the detected regions being displayed

Fig. 10 Forged image for original image using normal detection method

Step 4: Copy-move regions have been identified with two black spots using Gaussian detection method (Fig. 11).

Table 1 Accuracy comparison of different detection methods

Fig. 11 Forged image for original image using Gaussian detection method

Step 5: Copy-move regions have been identified with two black spots using intensity variation method (Fig. 12).

Block size

Normal method

Motion blur method

Intensity variation method

Gaussian noise method

2x2

97.78%

71.95%

94.37%

88.45%

4x4

97.62%

71.22%

92.46%

86.51%

8x8

91.28%

68.24%

87.74%

80.84%

16 x 16

82.62%

57.90%

79.49%

68.73%

Figure 14 provides the graphical representation of the simulation results of Table 1 for different post-processing.

100 95 90

Accuracy (%)

85 80 75 70 65

Normal Motion blur Intensity variation Gaussian noise

60 55 50 Fig. 12 Forged image for original image using intensity variation method

2

4

6

8

10

12

14

16

Block Size

Step 6: Copy-move regions have been identified with two black spots using motion blurring method (Fig. 13).

Fig. 14 Graphical representation of the results of different post-processing.

CONCLUSIONS

Fig. 13 Forged image for original image using motion blurring method

Table 1 shown next furnishes a comparative evaluation of the relative efficacy of the various forgery detection methods for different image sizes based on actual simulation experiments.

The present research proposes a digital wavelet based region duplication forgery detection technique. With no prior knowledge of the image characteristics, the developed methodology is capable of efficiently identifying the duplicated regions even if the image is tampered with by applying processing algorithms on intensity variation, noise addition, blurring and so on. The approach herein utilizes the DWT to find similarities and dissimilarities in different blocks of an image for robust detection of image forgery. The simulation experience demonstrates that the method could be a viable option for region duplication and forgery detection. ACKNOWLEDGMENTS This research was supported in part by the Department of Computer Science, College of Arts and Sciences, Troy University, Montgomery, AL 36103, USA.

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