Deep Ensemble Learning for Fake Digital Image Detection: A Convolutional Neural Network-Based Approach

Abstract

The advent of deep learning (DL) technologies has paved the way for a plethora of applications, the creation of hyperrealistic images via generative adversarial networks (GANs) being a compelling example. However, these synthetic images, nearly indistinguishable from genuine ones to the human eye, can be exploited for nefarious activities such as cybercrime, extortion, politically motivated campaigns, propaganda, among others. This paper proposes a deep ensemble learning approach to detect such counterfeit images, aiming to mitigate the issues arising from deepfake multimedia. The impetus for engaging ensemble models stems from their capacity to reduce the generalization error of predictions, provided the foundational models exhibit diversity and independence. Consequently, the prediction error diminishes when an ensemble approach is deployed. In this study, the CASIA v2 benchmark datasets, comprising 12,323 color images (5,123 originals and 7,200 counterfeits), were utilized. This investigation employed an ensemble of 13 pre-trained CNN models. The ensemble technique amalgamates these models to form a comprehensive perceptual model, wherein each model contributes to the final outcome. The classification predictions from each model are considered a 'vote', with the majority verdict serving as the final prediction. The proposed methodology was also juxtaposed with prevailing techniques. Assessment of our approach's efficacy revealed a 100% accuracy rate, 97.75% precision, 87.46% recall, and 99.9% AUC, underscoring the improvement offered by the proposed system.