A method for image forgery detection based on error level analysis (ELA) technique

In the last years, there has been growing a large increase in digital imaging techniques, and their applications became more and more pivotal in many critical scenarios. Conversely, hand in hand with this technological boost, imaging forgeries have increased more and more along with their level of precision. In this view, the use of digital tools, aiming to verify the integrity of a certain image, is essential. Indeed, insurance is a field that extensively uses images for filling claim requests and a robust forgery detection is essential. This paper proposes an approach which aims to introduce a full-automated system for identifying potential splicing frauds in images of car plates by overcoming traditional problems using artificial neural networks (ANN). For instance, classic fraud-detection algorithms are impossible to fully automatize whereas modern deep learning approaches require vast training datasets that are not available most of the time. The method developed in this paper uses Error Level Analysis (ELA) performed on car license plates as an input for a trained model which is able to classify license plates in either original or forged.