Asst Prof

J Monica Esther

PG P Sabeena Burvin

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IOSR Journal of Computer Engineering (IOSR-JCE) 
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. XI (Mar-Apr. 2014), PP 10-13 
www.iosrjournals.org 
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Analysis of Digital Image Splicing Detection 
 
P.Sabeena Burvin, PG scholar,   J.Monica Esther, Asst Prof,                                                               
Department of Information Technology, Francis Xavier Engineering College, Tirunelveli 
Department of Information Technology,  Francis Xavier Engineering College,Tirunelveli. 
 
Abstract: The availability of photo manipulation software has made it unprecedentedly easy to manipulate 
images for malicious purposes. One of the most common forms of digital image or photographic manipulation 
operation is known as image splicing or image composition. It is a process that’s crops and paste regions from 
same or separate sources. In this paper, we analyze various automatic image forensic techniques for detecting 
digital image splicing. 
Keywords: image forensic, image forgery, image composition, image retouching, copy-move image forgery 
 
I.    Introduction 
                 Digital Image Forgery Detection is an important field in Image Processing, because digital images are 
used in many social areas like in courts where they are used as evidence. In information channels like 
newspapers, magazines, websites and televisions, digital images are powerful tool for communication. 
Unfortunately, it is easy to use computer graphics, image editing software and image processing techniques to 
manipulate the images. These manipulated images create some unwanted problems in information channels.              
              Basically there are three different types of digital image forgery: Copy-Move image forgery, Image 
Splicing image forgery and Image Retouching image forgery. In Copy-Move image forgery, one part of the 
image is copied and pasted on other part of the same image [1]. In other words, the source and destination of the 
modified image originated from the same image. This is usually done in order to conceal certain details or the 
duplicate certain aspects of an image. Figure (1) shows an example of copy move image forgery. 
 
1a) Original image                   1b) Forged image 
Figure1. An example of copy move image forgery 
                In Image Splicing, two images are combined to create one tampered image or it is a technique that 
involves a composite of two or more images, which are combined to create a fake image [2]. Figure (2) shows 
an example of image splicing image forgery.  
 
 
2a) Original image                     2b) Forged image 
      Figure2. An example of image splicing image forgery 
Analysis of Digital Image Splicing Detection 
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3a) Original image                         3b) Forged image 
Figure3. An example of image retouching image forgery 
                 In Image Retouching, the images are less modified. It just enhances some features of the image. 
There are several subtypes of digital image retouching, mainly technical retouching and creative retouching [3]. 
Figure (3) shows an example of image retouching image forgery. 
 
II.  Image Splicing 
                 In this paper, we analyze about image splicing image forgery. Image splicing is a common form of 
digital image manipulation or image forgery. It is one such type of tampering; Image splicing is also called as 
image composition. The basic definition of image splicing is a process that crops and paste regions from same or 
separate sources. The following figure (4) shows one of the example form of image splicing process and its 
steps [4]. 
 
Figure4. The steps of image splicing, f(x, y) and g(x, y) are original images, h(x, y) is a part of f(x, y) which is 
insert into g(x, y) and generate spliced image I(x, y). Perhaps f(x, y) and g(x, y) is the same image. 
 
5a.Source image            5b.Target image       5c.Composite image 
Figure5: The Process of Image Splicing Forgery 
                     As shown in above figure (5), by coping a spliced portion from the source image (5a) into a target 
image (5b). One can create a composite picture (5c) or scenery to cheat others with the help of state-of-art image 
editing software [5], even non-professional users can perform splicing without much difficulty. 
 
III.   Analysis of Image Splicing Detection 
                    Zhehua and Guoping proposed an automatic detection framework to identify a spliced image, which 
is fully based on Human Visual System model in which visual saliency and fixation are used as a forensic cue 
[6]. This technique provides a convenient way to locate splicing boundaries, but this system needs some training 
for users. 
                  Servinc and Ismail proposed a method based on the neighbor bit planes of the image. The basic idea 
is the correlation between the bit planes as well as the binary texture characteristics within the bit planes will 
differ between an original and a spliced image, which is considered for detecting splicing [7]. This method 
Analysis of Digital Image Splicing Detection 
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provides better performance at both stronger and weaker level of manipulation, but in order to identify the 
spliced image this method requires various image forensic detectors. 
                 Farid method consider the fact that while creating a digital composite image the matching of lightning 
conditions of digital image is difficult between the individual photographs[8], this inconsistencies in lighting can 
be used as a factor for detecting digital tampering. But the main drawback with this approach is it is not 
applicable for indoor images.  
      Alin and Farid proposed a method to detect the spliced image by detecting traces of resampling. 
When creating digital forgeries, it is often necessary to scale, rotate or distort a portion of an image. This 
process involves resampling the original image onto a new lattice, which resampling rates used for exposing 
digital forgeries [9]. This method offers a complementary approach to authenticating digital images, but it’s only 
applicable for uncompressed TIF &JPEG image with minimal compression.  
     Alin and Farid again proposed an automatic method for detecting traces of digital tampering in 
lossless & lossy compressed image by using color Filter Array[10], Most digital cameras employ a single sensor 
in conjunction with a CFA, where the missing color samples are  interpolated from these recorded samples to 
obtain a three channel color image. This interpolation introduces specific correlations which are likely to be 
destroyed when tampering with a digital image. As such, the presence or lack of correlations produced by CFA 
interpolation can be used to declare it as a forgery. This technique works in the absence of any digital watermark 
or signature to authenticating digital images, but this will not be the case in practical. 
    Johnson and Farid proposed a method based on the specular highlights that appear on the eye are a 
powerful cue to shape, color and location of the light source [11].Inconsistencies in these properties of light can 
be used as evidence of tampering. It can applicable to arbitrary objects, but this method only determines the 
direction to the light source within one degree of ambiguity. 
  Riers & Angelopoulou proposed illumination color as a new indicator to distinguish the original and 
manipulated image [12]. The basic idea is that if an image has been manipulated, the transition between the 
illuminants should be disturbed. The disturbed illuminants can be used as a best indicator for identifying image 
authenticity; but it requires the original image for comparison process. 
 Feng and chang proposed a fully automatic spliced image detection method, which is based on 
consistency checking of camera characteristics among different areas in an image [13], From each area using 
geometric invariants camera response functions (CRF) is determined. The boundary between the authentic or 
spliced regions is segmented by classifiers. This method provides high performance, but the rate of detection of 
splicing is not to the expected level. 
  Chennamma & Lalitha proposed a detection method based on the spherical lens which introduces 
radial distortion [14]. This radial distortion parameter estimated and their consistency is verified for the 
detection of splicing. The efficiency of this method depends on the resolution of the images. 
  Eric and Hany put forth a method which estimates the lightning conditions of the digital composite 
image in 3-D environment and the same estimate is used as a evidence for detecting tampering [15]. This 
method is very powerful in tampering analysis. 
 Farid & Bravo proposed an automatic detection method for identifying faked pictures without the 
involvement of HVS (Human Visual System) [16], which shows the visual system is remarkably improper to 
detecting geometric inconsistencies in shadows, reflection and perspective distortion. But the process of 
detection is time consuming. 
 Xuemin & Zhen locate the splicing area by exploiting illuminant color inconsistency [17]. This method 
detects image splicing and also locates the spliced region, is not applicable for indoor images and original image 
is necessary to find the duplication or manipulation. 
 Pravin, Sudha and Ser proposed a novel method for detecting image splicing using discrepancies in 
motion blur. In which they used motion blur estimation through image gradients in order to detect 
inconsistencies between the spliced region and rest of the image [18]. This method needs very less human 
intervention, improves robustness and efficiency. 
 Shinteng & Tszan proposed a fast and effective forgery detection technique for copy-move and image 
splicing forgery, which focuses on JPEG format image and detect both image forgeries [19]. This method is 
mainly used to find the Copy-and-Paste operation of image regions from one image onto another. 
 James F.O’Brien & Harry proposed a new forensic technique has emerged to detect geometric or 
statistical inconsistencies that result from specific forms of Photographic manipulation [20], but the human 
visual system is needed to identify the corresponding points on objects and it’s  reflections.  
              Pan, Xing & Lyu describes a method based on the fact that the images from different origins tend to 
have different amount of noise introduced by the sensors [21]. They proposed an effective method to expose 
image splicing by detecting the inconsistencies in local noise variances. But the main this method is not able to 
detect the entire tampered region. 
Analysis of Digital Image Splicing Detection 
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  Kekre, Misha, Pallavi, Shede & Gupta proposed a new method based on image hashing, which is used 
to generate hash value for each image in the database [22], these hash values can be used for content based 
image retrieval, image database indexing, image authentication and also avoiding & mitigating the tampering of 
digital images. The only limitation of this technique is the original image is needed for find duplication. 
             Zahra Mohamadian and Ali Akbar pouyan proposed a method to detect forgery in digital images in 
uniform and non-uniform regions in which Zernike moments based detection approaches is used to detect flat 
copied regions [23]. If the images are rotated, scaled or distorted this method will fail to identify the forged 
image. 
 Munkhbaatar & Rhee proposed a blind forgery detection scheme using compatibility metrics based on 
edge blur and lightning directions [24]. The edge blur width is used to identify the discontinuities of edge in 
spliced image and the lightning directions are used to enlighten the image composition. 
 
IV.   Conclusion 
 Nowadays image splicing image forgery is becoming a common way the anti-social people are using to 
create the fake photographs and misusing them. So it is necessary to identify such kind of image manipulations. 
So many researches have already been carried out on image splicing. But the existing methods of detecting 
image splicing undergo the following challenges: original image is essential for revealing tampering, forgeries 
with indoor image and image resolution. We have analyzed several methods proposed in research papers to 
detect image composition to insist the necessity for image splicing detection. This field is still growing and a lot 
of research is needed to make Digital forensic more promising.                         
 
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Analysis of Digital Image Splicing Detection

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