AN ADAPTIVE COMPRESSION FACTOR ERROR LEVEL ANALYSIS FOR IMAGE FORGERY CLASSIFICATION

Abstract

The intentional manipulation of visual data has been increasing due to the widespread use of image editing software and social media websites, challenging existing forgery detection methods. Error Level Analysis (ELA) based methods often struggle with JPEG compression, limiting their ability to detect tampering accurately. This paper proposes an adaptive compression mechanism to enhance ELA-based image forgery detection, particularly for augmented and expanded datasets. Using the CASIA V2 image forgery dataset with rotation, flipping, and scaling, ELA maps were derived and classified via a Convolutional Neural Network (CNN). The experimental results indicate that the proposed method achieved a better performance with accuracy, precision, recall, and F1-score of 96.6%, 96.8%, 96.3%, and 96.5%, respectively.