Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering
This addresses document forgery detection for forensic analysis, but it is incremental as it applies an existing clustering method to hyperspectral data.
The paper tackles forgery detection in hyperspectral document images by using K-means clustering to identify different inks based on their unique spectral signatures, enabling forensic experts to assess document authenticity.
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.