A CNN based architecture for forgery detection in administrative documents
Khadidja Maamouli, Hiba Benhamza, Abdelhamid Djeffal, Abbas Cheddad · 2022
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% recall, surpassing the efficiency of the machine learning base methods.