Journal ArticleDOI
Decision-theoretic model to identify printed sources
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TLDR
The proposed decision-theoretical model can be very efficiently implemented for real world digital forensic applications and is superior to the existing approaches.Abstract:
When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. Over the past several years, digital forensics for printed document source identification has begun to be increasingly important which can be related to the investigation and prosecution of many types of crimes. Unlike invasive forensic approach which requires a fraction of the printed document as the specimen for verification, noninvasive forensic technique uses the optical mechanism to explore the relationship between the scanned images and the source printer. To explore the relationship between source printers and images obtained by the scanner, the proposed decision-theoretical approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Consequently, the proposed aggregation method intensively applies the extracted features and decision-fusion model of feature selections for classification. In addition, the impact of different paper texture or paper color for printed sources identification is also investigated. In the meantime, the up-to-date techniques based on deep learning system is developed by Convolutional Neural Networks (CNNs) which can learn the features automatically to solve the complex image classification problem. Both systems have been compared and the experimental results indicate that the proposed system achieve the overall best accuracy prediction for image and text input and is superior to the existing approaches. In brief, the proposed decision-theoretical model can be very efficiently implemented for real world digital forensic applications.read more
Citations
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Journal ArticleDOI
Neural networks for image transformation, analysis, and compression☆
Journal ArticleDOI
Deep learning for printed document source identification
TL;DR: A deep learning system to solve the complex image classification problem is developed by Convolutional Neural Networks (CNNs) of deep learning which can learn the features automatically and should be constantly evaluated and compared for the best interest in universal utilization.
Journal ArticleDOI
Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents
TL;DR: A set of features for characterizing text-line-level geometric distortions is proposed and a novel system to use them for identification of the origin of a printed document and gives much higher accuracy under small training size constraint is presented.
Journal ArticleDOI
A computational approach for printed document forensics using SURF and ORB features
TL;DR: A classifier-based model to identify the source printer and classify the questioned document in one of the printer classes is proposed and can efficiently classify the questioning documents to their respective printer class.
Journal ArticleDOI
Source Printer Classification Using Printer Specific Local Texture Descriptor
Sharad Joshi,Nitin Khanna +1 more
TL;DR: In this paper, a printer specific local texture descriptor is introduced to overcome the limitation that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier.
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