Detecting documents forged by printing and copying

Shang, Shize; Memon, Nasir; Kong, Xiangwei

doi:10.1186/1687-6180-2014-140

Cited by 44 publications

(31 citation statements)

References 23 publications

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“…In addition, some specific methods have been designed to classify printing technique i.e. laser, inkjet or photocopier [3], [25]- [30]. A more detailed literature review of related techniques has been discussed in [2] and [7].…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Source Printer Classification Using Printer Specific Local Texture Descriptor

Joshi

Khanna

2020

IEEE Trans.Inform.Forensic Secur.

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The knowledge of source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. Development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining lot of attention in multimedia forensics. Currently, state-of-the-art systems require that the font of letters present in test documents of unknown origin must be available in those used for training the classifier. In this work, we attempt to take the first step towards overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font, and 2) on another dataset 1 having documents printed in four different fonts, the proposed method correctly classifies all test samples when sufficient training data is available in same font setup. In addition, it outperforms state-of-the-art methods for cross font experiments. Moreover, it reduces the confusion between the printers of same brand and model.

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Source Printer Classification Using Printer Specific Local Texture Descriptor

Joshi

Khanna

2020

IEEE Trans.Inform.Forensic Secur.

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“…Every printer uniquely affects the position and size of characters while printing [28]. Moreover, different brands of printers produce various types of geometric distortion 5 signatures which are consistently retained for a long time [28]. These distortions in a printed document are quantized by estimating offsets in location and size of characters with respect to their electronic version.…”

Section: Geometric Distortion Featuresmentioning

confidence: 99%

Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents

Jain

Joshi

Gupta

et al. 2019

Multimed Tools Appl

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In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions, their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of thirteen printers demonstrate that the proposed system achieves state of the art performance and gives much higher accuracy under small training size constraint. For four training and six test pages of three different fonts, the proposed method gives 99% classification accuracy.

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“…Lampert and Breuel 19 extracted a set of 15 features from each character. Shang et al 23 proposed a new method to distinguish laser printing, inkjet printing, and photocopying techniques. These features are distinct between laser and inkjet printed characters.…”

Section: Source Of Device Typesmentioning

confidence: 99%

“…[19][20][21][22][23] Such devices might have one laser printer, inkjet printer, or photocopier. In document forgery detection, some methods focus on device type identification.…”

Section: Introductionmentioning

confidence: 99%

Document forgery detection using distortion mutation of geometric parameters in characters

Shang

Kong

You

2015

J. Electron. Imaging

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Tampering related to document forgeries is often accomplished by copy-pasting or add-printing. These tampering methods introduce character distortion mutation in documents. We present a method of exposing document forgeries using distortion mutation of geometric parameters. We estimate distortion parameters, which consist of translation and rotation distortions, through image matching for each character. Detection of tampered characters with distortion mutation occurs based on a distortion probability, which is calculated from character distortion parameters. The introduction of a visualized probability map describes the degree of distortion mutation for a full page. The proposed method exposes the forgeries based on individual characters and applies to English and Chinese document examinations. Experimental results demonstrate the effectiveness of our method on low JPEG compression quality and low resolution. Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 023008-2 Mar∕Apr 2015 • Vol. 24(2) Shang, Kong, and You: Document forgery detection using distortion mutation of geometric parameters in characters Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 023008-4 Mar∕Apr 2015 • Vol. 24(2) Shang, Kong, and You: Document forgery detection using distortion mutation of geometric parameters in characters Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 023008-5 Mar∕Apr 2015 • Vol. 24(2) Shang, Kong, and You: Document forgery detection using distortion mutation of geometric parameters in characters Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 023008-6 Mar∕Apr 2015 • Vol. 24(2) Shang, Kong, and You: Document forgery detection using distortion mutation of geometric parameters in characters Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 023008-8 Mar∕Apr 2015 • Vol. 24(2) Shang, Kong, and You: Document forgery detection using distortion mutation of geometric parameters in characters Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

show abstract

Detecting documents forged by printing and copying

Cited by 44 publications

References 23 publications

Source Printer Classification Using Printer Specific Local Texture Descriptor

Source Printer Classification Using Printer Specific Local Texture Descriptor

Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents

Document forgery detection using distortion mutation of geometric parameters in characters

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