Forged document detection and writer identification through unsupervised deep learning approach

Abstract

In recent years, there has been a signifcant increase in document forgery, which includes the fraudulent replication of currency, diplomas, and works of art. This has become a major issue due to the widespread usage of paper-based documentation. Handwriting is closely linked to document forgery and forensics as it possesses unique characteristics, including variations in text characters, pen pressure, writing angle, and stroke patterns, which makes it impossible to replicate accurately. As a result, handwriting serves as a personalized biometric that can be used to determine the authenticity of documents. However, traditional methods of writer identifcation are both time-consuming and destructive, requiring substantial expertise. To overcome these limitations, the study explores the potential of hyperspectral imaging (HSI) as a non-destructive and advanced approach for detecting and preventing document forgery. HSI provides detailed spectral information from a scene, making it possible to capture subtle spectral diferences in handwriting samples. This imaging technique has diverse applications in various felds such as agriculture, environmental monitoring, remote sensing, forensics, document analysis, and medical imaging. Our study proposes a novel unsupervised approach, CAE-SVM that uses Convolutional Autoencoder (CAE) for feature extraction and Support Vector Machine (SVM) for writer identifcation. It was tested on the UWA writing ink hyper spectral images dataset for blue and black inks which is available publicly and compared with state-of-the-art methods and CNN. The proposed approach achieved the highest accuracy of 92.78% for blue ink, surpassing existing methods. The study’s results emphasize the efcacy of HSI as a potent forensic analysis tool for detecting and preventing document forgery