Abstract: To pursue high efficiency and high accuracy in authentication of cigarette packets, an authentication model was established based on computer vision and machine learning. Computer vision was adopted to process the images of cigarette packet and extract feature vectors, a similarity measurement model and a machine learning model were used separately to classify the feature vectors and to discriminate between genuine and fake cigarettes. The similarity measurement model made classification with model of Manhattan distance, and optimized the parameters in Gauss bilateral filter function. Machine learning model was based on image segmentation, and determined the optimal amount and area of segment. A total of 603 samples of three cigarette brands, "CHUNGHWA", "YUXI"and"HEHUA"were identified by the two models separately. The results showed that the accuracy of the similarity measurement model for the test set of brand"YUXI"was 96.17%, and the accuracies of the machine learning model for the test sets of brands"CHUNGHWA", "YUXI"and "HEHUA"reached 98.99%, 96.61% and 100% respectively. Comparing with the similarity measurement model, the machine learning model has better migration ability and robustness, it is suitable for the authentication of cigarette samples of large amount, multiple categories and complex images. This method provides a technical support for improving the efficiency and accuracy of cigarette authentication.