In order to accurately and quickly identify the authenticity of the slightly tampered packaging printed logos with the help of mobile phones, an authenticity discrimination algorithm based on Siamese parallel attention convolutional neural network is proposed. The algorithm can reduce the representation bias of the network system through the shared weight mechanism of the Siamese network, improve the ability to extract minor tampering features through the parallel attention mechanism, and minimize the influence of noise introduced by printing and photographing on tampering feature extraction. The logo authenticity dataset with tampering area of 0.4% to 0.7% was established by photographing multiple forged logo printed images in 8 scenes combining 2 types of printing papers, 2 kinds of shooting light sources and 2 mobile devices. The discrimination accuracy of the model on the Print-Photo dataset is more than 94%, and the discrimination accuracy on the real counterfeit trademark is 100%. The experimental results show that the Siamese parallel attention convolutional neural network has high fine-grained discrimination accuracy and strong generalization ability, and can effectively realize the identification of the authenticity of packaging printed logos based on image minor tampering in open scenarios.