A hybrid model of convo-GAN to detect fake images

Abstract

With advancements in the field of Deep Learning, it has become easy to generate face swaps, thereby creating fake images which look extremely realistic, leaving few traces which cannot be detected by bare human eyes. Such images are known as ‘DeepFakes’ that can be used to create a ruckus and affect the quality of public discourse on sensitive issues, defame an individual’s profile, create political distress, blackmail a person or envision fake cyber terrorists. This paper proposes methods to detect fake images with the help of hybrid models having Convolutional Neural Network with Error Level Analysis, Gated Recurrent Unit neural network, Long Short Term Memory recurrent neural network and Generative Adversarial Network respectively. The 2019 ‘Real and Fake Face Detection’ dataset from Kaggle [7] is used to train the models and by experimentation we are able to prove that the combined model of Convolutional Neural Network and Generative Adversarial Network outperforms other models. © Grenze Scientific Society, 2021.