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A Study to Ascertain and Differentiate between Genuine and Transplanted Documents/Signatures | OMICS International
ISSN: 2157-7145
Journal of Forensic Research

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A Study to Ascertain and Differentiate between Genuine and Transplanted Documents/Signatures

Manisha Mann*, Shukla SK and Shruti Gupta

Amity Institute of Forensic Sciences, Amity University, Noida, UP-201303, India

*Corresponding Author:
Manisha Mann
Amity Institute of Forensic Sciences
Amity University, Noida, UP-201303, India
Tel: +919910471429
E-mail: [email protected]

Received date: May 21, 2015; Accepted date: July 21 2015; Published date: July 26, 2015

Citation: Mann M, Shukla SK, Gupta S (2015) A Study to Ascertain and Differentiate between Genuine and Transplanted Documents/Signatures. J Forensic Res 6:293. doi: 10.4172/21577145.1000293

Copyright: © 2015 Mann M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Forensic Research


Digital forgery is very common these days because the use of digital document is increasing day by day and readily available software’s for manipulating the documents. It is very necessary to authenticate the integrity of digital document, whether it’s genuine or not. Various secure detection systems are made which involves use of algorithm, but this paper deals with examining simple features present in image/document which can be used to check the authenticity of an electronic document, whether they are genuine or forged. These simple features are like Color variation, Font size difference, Pixelate resolution, Range of magnification-low, medium and high range of magnification and extracting hash value-MD4, MD5, SHA-1, RIPEMD-128 and RIPEMD-160. Results are highly reliable.


Digital documents; Digital forgery; Image hashing; Pixels


Now a days use of electronic documents have been increased tremendously which results in increase in digital forgery. A forgery is an unlawful act of forging a document or item for the reasons of misrepresentation or trickery. Digital forgery is very common now, because digital images are not difficult to manipulate and alter due to easily available image processing and editing software’s. These days, it is feasible to include or exclude any important characteristics from a picture without leaving any conspicuous hints of altering. Digital forensics is the current topic which has received attention recently. Digital images play an important role in depicting and transferring the data easily, therefore new techniques for detection of forgery in digital images have been investigated. There is a little difference between image forgery and digital image forgery, as the digital image forgery deals with the digital image as compared to photographs which are used in image forgery. There are many different computer graphic editing software are available like Adobe Photoshop, GIMP (GNU Image Manipulation Program), and Corel Paint Shop, etc.

Areas like legal, criminal, journalism, medical requires the digital document to be authentic. So there is high demand for a dependable, safe and secure detection system, which is capable to determine or check whether the digital image/document is real or altered. As due to presence of easily available image editing software’s alteration could be done to the digital document and some modification may be impossible to be seen by human eye, these modification results in some core statistics changes in the digital document which can be detected.

Forgery techniques in digital images are classified into three main groups

• Copy Paste Forgery (Image splicing).

• Image retouching.

• Copy Move Forgery (Image cloning).

Copy paste forgery also known as image splicing: In this type an altered duplicate copy or a document is prepared with the help of an original image along with some additional images, for instance including particular area of the additional image to the original one, just to hide or manipulate the image.

Image retouching: In this type the forger manipulates the image in a way so that the modification in the content of the image becomes unnoticeable.

Copy move forgery also known as Image cloning: In this type of forgery a distinct part of an image is copied and moved to another part of the same image.

Usually there are two types of detection techniques or approaches are used. Following are the two techniques

Active method includes features like watermarking which helps in detecting digital tampering like name, signature, date, etc.

Passive method in this method digital image forgeries are detected without taking any use of the features or information of the original image [1].

Image hashing/Image fingerprinting

Image hashing or image fingerprinting is a procedure of providing value that is specific to particular image by examining its content. This fingerprint is basically a string which is assimilated with other fingerprint for possible matches [2]. There are two types of image hashing techniques:

Perceptual hashing: Perceptual hashing a technique which produces fingerprint of different multimedia like audio, video or image file with the help of algorithm. Perceptual hash functions (PHF) are most commonly used in area of digital forensics and protection against copyright infringement. Through PHF one can assimilate and map source the data with the help of correlation between the hashes. For instance, Wikipedia contains a database of hashes of online articles and books for which copyright is hold by the authors, when any Wikipedia users upload any document containing data of online books or article registered in that database will have same hash value and will match with each other and this can be used in flagging or pointing out plagiarism [3].

Cryptographic hashing: Cryptography is a technique in which a message authentication code (MAC) is produced with the help of hash function. Even a slight change in input message results in distinct hash value. Following are mostly used hash algorithm:

• MD (Message Digest Algorithm) - MD4, MD5.

• SHA (Secure Hash Algorithm) – SHA-0, SHA-1.

• RIPEMD (RACE Integrity Primitive Evaluation) – RIPEMD-160 [4].

Pixels: Pixel is derived from a word “picture element”. In a computer picture, a pixel is the simplest unit of programmable color. The size of the pixel relies on the resolution of the display screen. If the display is at its highest resolution, the physical size of pixel will be equal to dot pitch of display. If the resolution is less than the highest, then size of pixel will be larger than dot pitch [5].

Early studies on this topic were Weihai li; et al. developed a method for detection of copy paste forgery in manipulated JPEG pictures and also locates the position or the area of manipulation. This method works by extracting DCT block artefact grid and determining mismatch of grid [6]. Thirumagal, et al. proposed a forensic technique for detection of contrast enhancement (globally or locally applied) and by identifying the peculiarities of intrinsic fingerprint the histogram equalization in a picture can be detected [7]. Najah Muhammad, et al. proposed an effective non-intrusive technique for detection of copy move forgery. In this technique the image is segmented and the similarity is detected with the help of Dyadic Wavelet Transform (Dy WT) [8]. R. Venkatesan, et al. proposed an image indexing technique which is known as image hash function. Randomized signal processing is used by algorithm for a non-reversible compression of an image which results into arbitrary binary strings [9]. Kelsy Ramirez-Gutierrez, et al. proposed two algorithm to detect authenticity of an image, even if the image is affected by distortion like filtering, compression and other malevolent modification like geometric distortion. The algorithm can also detect tampering and also the localized tampered areas [10].

This paper deals with examining simple features present in image/ document which can be used to check the authenticity of an electronic document, whether they are genuine or forged. These simple features are like Color variation, Font size difference, Pixelate resolution, Range of magnification: low, medium and high range of magnification and extracting hash value: MD4, MD5, SHA-1, RIPEMD-128 and RIPEMD-160.

Material and Methods

Sample size

25 samples of documents were created which contained signatures, dates, names and addresses, which have been transplanted from the originals on those documents. Doctor’s prescription, list of student selected in any institution, stamp papers, certificates and appointment letters are the type of samples.

Sample collection

The 25 samples were collected from Google Images.

Procedure adopted for analysis

As original disputed documents cannot be gathered due to their authorization and confidentiality, which should be maintained by government forensic laboratories with due reason, such documents were prepared manually for the research with the help of software’s. These samples were then analyzed in soft copy format. Then the signatures were cropped from the originals, copied and pasted on the documents to be forged with the help of MS Paint. These forged documents were then examined in soft copy format with the help of Picasa. Following are the features on the basis of the samples were examined:

• Colour variation.

• Font size difference.


• Range of magnification: low, medium and high range of magnification.

• Hash value: MD4, MD5, SHA-1, RIPEMD-128 and RIPEMD-160

Software’s and application used for analysis

• Microsoft Paint or MS Paint is a simple graphics program that has been included with all versions of Microsoft Windows. This program can be in colour mode or two-colour, black-and-white, but there is no grayscale mode.

• Picasa 3.9 is an image organizer and image viewer for organizing and editing digital photos plus an integrated photo-sharing website, originally created by a company named Lifescape in 2002 and owned by Google since 2004.

• is a website which reveals the all types of hash value of any type of file like audio, video or any document. Basically it’s an online hash value calculator.

Samples (observed features)

Since the number of samples created and observed for this study is very large, it would not be convenient to attach all the samples and pictures of the features observed in each sample. Therefore, only a few samples are being attached with the zoomed in images of their parts showing some difference from the whole of the document, indicating different sources of origin (Figures 1-4).


Figure 1: Enlarged view of the Sample (A).


Figure 2: Enlarged view of the Sample (B).


Figure 3: Enlarged view of the Sample (C).


Figure 4: Enlarged view of the Sample (D).

Result and Discussion

The following 25 samples were examined on basis of different features in Picasa Software:

• Range of magnification: low, medium and high range of magnification.

• Colour variation.

• Font size difference.

• Pixelate resolution.

Above mentioned features are given in Table 1.


Table 1: Shows the observation made on examination of samples with transplanted signatures on basis of Range of magnification, Color variation, Font size difference and Pixelate resolution.

Range of magnification

All the samples were first analyzed by magnifying them. The range at which transplantation was first observed was considered as lowest range of magnification for the specific sample. Simultaneously the range of magnification was increased and it was observed that on increase of magnification the transplantation was more prominently seen. All the samples were analyzed between 26 to 1252 ranges of magnification.

Color variation and font size difference

All the samples were analyzed to check color variation in two aspects: color variation in paper background and color variation in ink. Difference in the font size of the samples was examined and observation was noted down in the Table 1. The samples which were transplanted had noticeable difference in their background/ink color as well as font size.

Pixelate resolution

All the samples were analyzed to detect difference in the pixelate resolution. It was found that the transplanted area had difference in their pixelate resolution as compared to the document on which they were transplanted.

All the samples (including the original document and simulated document) were analyzed using this application. Five type of hash values were calculated: MD4, MD5, RIPEMD-128, RIPEMD-160 and SHA-1. The change in the hash values of original and the simulated document were observed and the values were noted down in the Table 2.

Sample Original Sample Simulated Sample
1 MD4 34e60c92ce5cc5b80ffc86dffd636cc9 MD4 195522f215886931bcc957710df7b9da
MD5 547626b3ad63062faa47fc09b3a4bdf8 MD5 cc9399c13d221a29634609bb79a30068
RipeMD128 2fc0a2e99761f9b01823de9d078daf86 RipeMD128 caa0ab9adeb53aec7530b2f5907729d0
RipeMD160 a443a857fd7388a58ca874bfc15e070f96de83f8 RipeMD160 90b548d6de1ddf05853e9415bebcf44c13706785
SHA-1 c22c1e1ca93797651c4c12bf56c51d3b1774d409 SHA-1 8027cc878585510c8d01341bc3efa4c71e259125
2 MD4 c09f10c73a2e970c0d67dc8a5fca3f24 MD4 89f7abce148018daa6486be49077658e
MD5 627f26cb5e2b19fb7ac42c4f82c7344a MD5 ffbad43edfd1fb34e52afa33cbd35de9
RipeMD128 f1f804fdb6145b980ade9470126f249f RipeMD128 ce2259b7a0bf06db701adc4f280f8798
RipeMD160 190ebc7e27430377c71c6b45659bd3f4fd46cb79 RipeMD160 4d43ed392826af8d2dab585dc43156c3858b38ca
SHA-1 59313a8a1e2a68b9d977b47958e22f77d7297750 SHA-1 5200700a04963b99756762a1d3328cb8e10bbeed
3 MD4 8585091a427cd5f9f081543460e827e1 MD4 8cd64294745f69f6a2d495e52a844c57
MD5 11484a3e1860019303556a081835b194 MD5 ed9523be8127b46ab567f52fb0c69fd9
RipeMD128 029e1c3da86efd9ea16cff4e4364e2c3 RipeMD128 625eb0d620764900b533bba4c1d83cbd
RipeMD160 e2518f62b450f4118b015f38fa3e3ad9d6f5ae63 RipeMD160 c5c7fa61f56dc67a4effba113c4df113685db545
SHA-1 eaaf2ca6a0b54be2d073910e7dd7a30af7699d2b SHA-1 03381802d2537ef7a29fd9afe2b4f49b39271746
4 MD4 e485f01784b65e14210f7a19554ec68a MD4 8cd64294745f69f6a2d495e52a844c57
MD5 8ed4c472c40c71ef797aec695209d5fb MD5 ed9523be8127b46ab567f52fb0c69fd9
RipeMD128 0ef26f21f9588cf54678c63536c13cfc RipeMD128 625eb0d620764900b533bba4c1d83cbd
RipeMD160 e0740defb804dbcfb3c21d5ca743f18f5a533817 RipeMD160 c5c7fa61f56dc67a4effba113c4df113685db545
SHA-1 4311a59f99f976147b162f669adbed2c45765346 SHA-1 03381802d2537ef7a29fd9afe2b4f49b39271746
5 MD4 041c280894f1091486b0cfdd2dbe414b MD4 0f0da986daf5865624009206b10c1a34
MD5 4808eb48de6d2b662a2e3755f391f57b MD5 04cd4fc2232ba8a759f6d9e8c7c0d3d8
RipeMD128 b7249ecf67f84d3c1ede3e910169dd40 RipeMD128 6818554234382e32d62fafb14c778a0e
RipeMD160 729b5d6b47d92d6cc6d307dea3a76d95815b861a RipeMD160 fc57668923491d7ce97f30bece618ef9705920ee
SHA-1 d1a6c16dcb1667e5d2b2bcb619df989f1d1bcadc SHA-1 0367a2ec5e28408b77a6574f84eb9b8a09a7ad49
6 MD4 9288683235d520c02507cb5655e2228c MD4 dffd3733cc02b63ff1be9e30ca6967b8
MD5 d6ee7ad6027cfc7702f73fe660fcbe87 MD5 92a3fda4be8161cc080c0d25e9f02385
RipeMD128 2a6ffe4b2120a90ce3fa4eb53a6c5e24 RipeMD128 60499ea7222a4a34577b6cdd8690082c
RipeMD160 aff2963e22689179e0c43d1917ca9508c9472249 RipeMD160 c43c0375e08b8261d4bfd0b14528a88974771574
SHA-1 745b01a751e03c38839df2f1b7148a19dc85f6b4 SHA-1 88a05fddb16fb0e3c94349e4c43fc41d86e8d477
7 MD4 f89a32a2680ed68bd8a3ec6a2d2f6fe5 MD4 0a4cff65dda898c8b51c53510f8d7cd9
MD5 2aee4249a0b8c3aeb11c32d9e273e56b MD5 613bed51df2c2116c5936a92517087b1
RipeMD128 33a29d570a66710ac3ece05fdae2f0ee RipeMD128 014a1ff8044956e1ef7e6793db953e43
RipeMD160 54ff2e94aee2bb490222cb6a0ff95d23fa81f40c RipeMD160 771630a9b19a11afd492b44ce16800e0c0700531
SHA-1 2957a2ad9758fb45441f2f0dd2c032bdc715c5c8 SHA-1 4aa833ca95d0a626b6ac64e98d12e9de4911968c
8 MD4 3cf16e1061bbeac1e03e3a88ed903356 MD4 39b7ff75415e890377c6f8a87fa532a0
MD5 146ab572a19d38a5da9aa64817848545 MD5 1c4d242baf9cb7adabef4dfd449bab38
RipeMD128 6ea515264b83c420cce4af0170ce8e85 RipeMD128 6a42a2a81bfce86a2503a3c66c4254ae
RipeMD160 988b74357a2da53c85bb0b4101e3ee3dd291c22e RipeMD160 f56f7d2b4c900d9c218a3553915c6d8588dea231
SHA-1 7068093c2882207c6aca6e24fbe7b8deac23f97c SHA-1 03fe7355eed058ecf895a8e9b60d2b2f8b6def24
9 MD4 c5674db9ce02104f8efcfe9d217fe710 MD4 fc72f82425725abe7b5188c8214b66d5
MD5 45f48e5a5d032da442365d6abd41ca40 MD5 aff3fcffc4338db57dd3fa7d4c2c6914
RipeMD128 5c1a46be4319ecf35322d22ec31e01d0 RipeMD128 d6a904119b1dbc17ff7acc3f3edd8397
RipeMD160 64c6df70b0159a61d7aa11daf478f933e8c40dc9 RipeMD160 2b2b26fa59a1bca0fd94bae1134fa69cdb481710
SHA-1 e8bfdebbe85d028cd322668408aeb770cc58cb54 SHA-1 4cc9c72d46449365b49a4d1d0a7d234bde6073d3
10 MD4 467c5fa689a5d3ae39783979661a2c85 MD4 77937ee57e3ad8f161db9ae6e99ed9e5
MD5 d32295aa1408cff73d6187dbce68b1d1 MD5 34a490991d24fb8b1d3cd476ccc4eae9
RipeMD128 21a46949f3f17d9b324c3efa60a5c9d0 RipeMD128 4d6ba7204830a717cb87a261c2710763
RipeMD160 9719019953db085cb42eb72bd9e0ab60a33368fc RipeMD160 760cd8955df58e8460833819a0d73a93c1291fdd
SHA-1 215abf28bc44ccecbfd515c8bca55ed0de59c1a5 SHA-1 e69a8a6acfdc98e8c6dc6646beb35354779f939d

Table 2: Shows the observation made on examination of samples with transplanted signatures on basis of Hash values.


The present research reveals some simple methods which can help document experts as well as common man to establish whether the document is authentic or not. The results are extremely beneficial and reliable as after an appropriate magnification, color variation can be seen between transplanted area and the original document. Along with it on increasing the range of magnification the differences in font size and pixelate resolution are very much evident. The range of magnification was found to be between 26-1252%, which clearly determines the difference between the transplanted area and original document. The five types of hash values were also calculated to determine the difference between simulated document and the original one, the hash values were: MD4, MD5, RIPEMD-128, RIPEMD-160 and SHA-1. By adopting the material and methods used in this research, experts can be benefitted in dealing with softcopy transplanted forgery cases up to a huge extent.


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