Towards Drug Counterfeit Detection Using Package Paperboard Classification

Kauba, Christof; Debiasi, Luca; Schraml, Rudolf; Uhl, Andreas

doi:10.1007/978-3-319-48896-7_14

Cited by 9 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Buchanan et al [3] initially utilizes the diffuse scattering of focused laser to measure the fine structure of paper surface. Kauba et al [5] regarded the authentication of drug package as a paper texture classification problem using SVM. Schraml et al [6] further introduced open set recognition and extracted package texture from multi view images as combined features.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Paper Anti-Counterfeiting Method Based on Microstructure Orientation Estimation

Sun

Liao

Liu³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Forgery of paper invoices and certificates negatively causes huge economic loss every year. In the molding process of paper, plant fibres inside will distribute randomly and form the unique microscopic surface. Accordingly, forensic researchers have presented many techniques to build its feature descriptor for paper anti-counterfeiting. Among those methods, normal vector field and reconstructed surface based on multi-view photos captured by mobile camera have indicated incredibly effectiveness and practicality. However, the inefficiency of existing models using mobile camera is the neck in real time detection scenarios. In this paper, we focus on the efficient microstructure orientation estimation of paper surface for authentication. To address this problem, we investigate the reflection characteristics of rough surface and quantify the light attenuation accurately. Then, we model the entire propagation path of light emitted by flash, and exploit single photo captured by mobile camera to reconstruct microscopic inclination based on beam energy loss. Reduction of required pictures significantly enhances forensics efficiency. Through comparison experiments, we demonstrate that our proposed method can be used for paper discrimination effectively with speedy feature extraction.

show abstract

Section: Introductionmentioning

confidence: 99%