Deepfakes Detection Techniques Using Deep Learning: A Survey

Abdulqader M. Almars
2021 Journal of Computer and Communications  
Deep learning is an effective and useful technique that has been widely applied in a variety of fields, including computer vision, machine vision, and natural language processing. Deepfakes uses deep learning technology to manipulate images and videos of a person that humans cannot differentiate them from the real one. In recent years, many studies have been conducted to understand how deepfakes work and many approaches based on deep learning have been introduced to detect deepfakes videos or
more » ... ages. In this paper, we conduct a comprehensive review of deepfakes creation and detection technologies using deep learning approaches. In addition, we give a thorough analysis of various technologies and their application in deepfakes detection. Our study will be beneficial for researchers in this field as it will cover the recent state-of-art methods that discover deepfakes videos or images in social contents. In addition, it will help comparison with the existing works because of the detailed description of the latest methods and dataset used in this domain. Communications 2018, a fake video for Barack Obama was created to putting words he never uttered [4] . In addition, In the US 2020 election, deepfakes have already been used to manipulate Joe Biden videos showing his tongue out. These harmful uses of deepfakes can have a serious impact on our society and can also result in spreading miss leading information, especially on social media. Generative adversarial networks (GANs) [5] are generative and sophisticated deep learning technologies that can be applied to generate fake images and videos that hard for a human to identify from the true ones. Those models are used to train on a data set and then create fake images and videos. This kind of deepfake model requires a large set of training data for those deepfake media. The larger data set, the more believable and realistic images and videos can be created by the model. In fact, the large availability of presidents and Hollywood celebrity's videos on social media can help individuals to produce realistic fake news and rumors that can bring a serious impact on our society. Recent studies show that deepfake video and images have become heavily circulated through social channels. Detection of deepfake videos and images, therefore, has become increasingly critical and important. To encourage researchers, many organizations such as United States Defense Advanced Research Projects Agency (DARPA), Facebook Inc and Google launched a research initiative in attempting the detection and prevention of deepfake [6] [7] . As a result, many deep learning approaches such as long short-term memory (LSTM), recurrent neural network (RNN) and even the hybrid approaches has been proposed to in order to detect deepfakes images and videos and to bring up more research over this field [8] [9] [10] [11] . The current studies show that deep neural networks made a remarkable result in terms of detecting fake news and rumors in social media posts. This work primarily focuses on providing a comprehensive study for deepfake detection using deep-learning methods such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long short-term memory (LSTM). This survey will be useful and beneficial for researchers in this field as it will give: 1) details summary of the current research studies; 2) datasets used in this field; 3) the limitations of the current approaches and insights of future work. The contributions of our survey are summarized as follows.  This is the first review that covers the current deep learning methods for deepfake detection and discusses their limitations.  In this survey, we also cover all the challenges that are faced by the researchers in this area and provide an outlook of future directions.  In this review, we summarize and present available annotated datasets that are used in this field. The rest of the article is structured as follows: Section 2 summaries the related work, Section 3 contains deepfake Creation and deep learning Detection Techniques, Section 4 contains the public available dataset used in the Deepfake field, the challenges and the open issues are discussed in Section 5, Section 6 concludes the research. A. M. Almars
doi:10.4236/jcc.2021.95003 fatcat:4zpksmozhrdmjh6e25l2g3jjru