Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering

TL;DR

This paper presents a hyperspectral imaging-based method using K-means clustering to detect ink forgery in documents by differentiating inks with similar visual appearance but distinct spectral signatures.

Contribution

It introduces an extensive ink mismatch detection technique leveraging K-means clustering on hyperspectral data to identify and separate different inks based on their spectral responses.

Findings

Effective differentiation of visually similar inks.

Successful separation of inks into distinct spectral clusters.

Potential for forensic document authentication.

Abstract

Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.