Fake face detection in video using shallow deep learning architectures

Abstract

Deep learning techniques have been used in various disciplines, ranging from simple data processing to complicated image classification tasks. Deepfakes is a deep learning approach with benefits and drawbacks impacting the world. However, deepfakes are now cutting-edge technology being exploited for nefarious purposes such as the breach of human privacy and identity. Because deep learning is advancing rapidly daily, people use AI to produce deepfakes videos and images. Hence newer AI technology to detect deepfakes is critical. Therefore, the study has proposed detecting video and image deepfakes based on convolutional neural network (CNN) combined with the long short-term memory (LSTM) model, which constructs a deep learning model classifying images and video deepfakes. The proposed model investigated a novel approach to research more powerful models which can be applied to any large dataset. The experimental results demonstrated that the proposed method had achieved promising performance on modified datasets from Celeb-DF with high AUC performance up to 0.7584 and MCC reaching 0.558. Besides, this paper presents brief research on creating and detecting the image and video deepfakes technologies and points out the challenges of using deepfakes in many different contexts.