On Microstructure Estimation Using Flatbed Scanners for Paper Surface Based Authentication
Runze Liu, Chau-Wai Wong

TL;DR
This paper investigates how flatbed scanners can be used to extract microscopic surface features of paper for authentication, demonstrating the effectiveness of heightmap features over norm maps and establishing a relationship between patch size and authentication accuracy.
Contribution
It provides an analytical and empirical analysis of feature extraction methods for paper surface authentication using flatbed scanners, highlighting the superiority of heightmap features and quantifying patch size effects.
Findings
Heightmap features outperform norm maps in authentication.
Specular reflection does not affect norm map estimation with flatbed scanners.
A linear relationship exists between patch size and log(EER) in authentication performance.
Abstract
Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a "fingerprint" of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those…
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Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Digital Media Forensic Detection · Image and Object Detection Techniques
