Rama Rao Adimalla 3 3 (July - September) In this paper a general overview of the use of deep learning in the battle against the booming deepfake industry problem is discussed Uses of deep learning include but are not limited to natural language processing machine learning and computer vision and are responsible for a multitude of novel applications Meanwhile the growing number of convincing videos and images has also been worrying This technology can have serious implications online when used for nefarious purposes such as fake news stories persona impersonation financial scams and distribution of unwanted explicit visual material Those such as celebrities and politicians are particularly vulnerable to this The current work evaluates the performance of four deep learning models namely InceptionResNetV2 VGG19 a standard CNN and Xception in the fields of deepfakes generation and identification The evaluation carried out using a Kaggle dataset of deepfakes confirms that Xception outperforms the other models analysed in detecting deepfakes In a rapidly evolving landscape where malicious deepfakes are becoming more common every day it is necessary to develop dependable detection systems to reduce the impact they can have on society Copyright (c) International Journal of Computational Science and Engineering Research . All rights reserved 2026