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Texture to the rescue: Practical paper fingerprinting based on texture patterns

Lookup NU author(s): Ehsan Toreini, Professor Feng Hao

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Abstract

© 2017 ACM. In this article, we propose a novel paper fingerprinting technique based on analyzing the translucent patterns revealed when a light source shines through the paper. These patterns represent the inherent texture of paper, formed by the random interleaving of wooden particles during the manufacturing process. We show that these patterns can be easily captured by a commodity camera and condensed into a compact 2,048-bit fingerprint code. Prominent works in this area (Nature 2005, IEEE S&P 2009, CCS 2011) have all focused on fingerprinting paper based on the paper "surface." We are motivated by the observation that capturing the surface alone misses important distinctive features such as the noneven thickness, random distribution of impurities, and different materials in the paper with varying opacities. Through experiments, we demonstrate that the embedded paper texture provides a more reliable source for fingerprinting than features on the surface. Based on the collected datasets, we achieve 0% false rejection and 0% false acceptance rates. We further report that our extracted fingerprints contain 807 degrees of freedom (DoF), which is much higher than the 249 DoF with iris codes (that have the same size of 2,048 bits). The high amount of DoF for texturebased fingerprints makes our method extremely scalable for recognition among very large databases; it also allows secure usage of the extracted fingerprint in privacy-preserving authentication schemes based on error correction techniques.

Publication metadata

Author(s): Toreini E, Shahandashti SF, Hao F

Publication type: Article

Publication status: Published

Journal: ACM Transactions on Privacy and Security

Year: 2017

Volume: 20

Issue: 3

Online publication date: 11/08/2017

Acceptance date: 02/04/2016

Date deposited: 24/01/2018

ISSN (print): 2471-2566

ISSN (electronic): 2471-2574

Publisher: Association for Computing Machinery

URL: https://doi.org/10.1145/3092816

DOI: 10.1145/3092816

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