Automotive paint is one of the most important evidence in solving vehicle‐related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral‐domain optical coherence tomography system (SD‐OCT). Two‐dimensional cross‐sectional OCT images and three‐dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A‐scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross‐sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A‐scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi‐layer perceptron (MLP), k‐nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.

Counterfeiting of banknotes is a financial crime that severely affects economic security and social stability [1]. The key to combat banknote counterfeiting crimes is to detect and link the counterfeit banknotes to the counterfeiters, which remains a challenge due to the continuous development of printing technologies and scanner systems. The advanced counterfeiting techniques can vividly reproduce multiple security features, such as security thread, intaglio, serial number, watermark, and microtext. Therefore, it is urgent to