Maritime digital KYC processes are susceptible to various face spoofing attacks. When any unauthorized person tries to enter in the authentication system by presenting a fraud image and/or video, it is termed as a spoofing attack. Face anti-spoofing attacks have been typically approached from texture based models (e.g. Local Binary patterns) combined with machine learning (e.g. KNN) approaches. The aim of this study is to build a robust face anti-spoofing system using deep convolutional neural networks for maritime digital KYC processes. The research is based on analyzing the features of genuine and fake images. We use the freely available NUAA photograph imposter database for our face anti-spoofing study. The database has respectively 7500 and 5100 labelled imposter and client face images. We split the dataset into train and test sets with an 80%-20% split ratio using stratified sampling. 2D convolutional layers combined with 2D MaxPooling layers followed by Flattening and Dense layers are employed for our deep network architecture. The research is carried out using scikit-learn and keras open-source libraries for python. The training accuracy of the reported model is 100% and the testing accuracy is 99.92%. The accuracy of our present deep learning approach surpasses the accuracy of all the models available in literature.