With the wide spread of digital document use in administrations, fabrication and use of forged documents have become a serious problem. This paper presents a study and classification of the most important works on image and document forgery detection. The classification is based on documents type, forgery type, detection method, validation dataset, evaluation metrics and obtained results. Most of existing forgery detection works are dealing with images and few of them analyze administrative documents and go deeper to analyze their contents.

The use of digital documents is knowing a widespread in different daily administrative and economic transactions. Simultaneously, the forgery of many documents becomes a crime that costs billions to states and companies. Several researchers tried to develop techniques that automatically detect forged documents using machine learning and image processing. With the immense success of deep learning applications, we employ, in this work, a convolutional neural network architecture that uses a gathered dataset of forged and authentic administrative documents. The results obtained on our dataset of 493 documents reached 73.95 % accuracy and 97.3

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