A computationally efficient hybrid Kolmogorov-Arnold network for hyperspectral classification of signatory pen inks

In the field of forensic document examination, the non-destructive and precise differentiation of signing pen inks is pivotal for determining document authenticity and reconstructing case facts. Hyperspectral imaging (HSI), characterized by the integration of spatial and spectral information, has become a vital tool for ink analysis; however, the massive volume of high-dimensional data and significant inter-band redundancy pose challenges for traditional classification algorithms. Herein, we propose integrating a convolutional neural network (CNN) and a Kolmogorov-Arnold network (KAN) into a hybrid CNN-KAN architecture that employs the CNN as its front-end to extract local hierarchical features and compress dimensions and the KAN module as its back-end to enhance non-linear fitting capabilities, thereby effectively discriminating between highly similar ink categories. This approach addresses the limited fitting capacity of standalone CNNs and mitigates the excessive computational costs associated with applying pure KANs to high-dimensional data. Experimental results demonstrate that in a classification task involving 40 brands and models of signing pen inks, the CNN-KAN model achieved an accuracy of 98.56%, outperforming traditional CNN, Visual Geometry Group (VGG), feedforward neural network (FNN), and attention-guided U-shaped network (AUNet) models. Furthermore, compared to a pure KAN, the proposed hybrid architecture reduced the computational load by approximately 60%, achieving a balance between high precision and computational efficiency. This study provides a superior deep learning solution for forensic evidence identification based on HSI technology.