Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Document Frauds: Identification and Linking Fake Document to Scanners and Printers
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Digital Investigation: The International Journal of Digital Forensics & Incident Response
A survey of forensic characterization methods for physical devices
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
Color laser printer forensic based on noisy feature and support vector machine classifier
Multimedia Tools and Applications
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This paper presents a detection scheme for a fraudulent document made by printers. The fraud document is indistinguishable by the naked eye from a genuine document because of the technological advances in printing methods. Even though we cannot find any visual evidence of forgery, the fraud document includes inherent device features. We propose a method to uncover these features. 17 image quality measures are applied to discriminate between genuine and fake documents. The results of each measure are used as training and testing parameters of SVM classifier to determine fake documents. Preliminary experimental results are presented based on the fraud gift voucher made by several color printers.