![]() Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories (2) how they could be created and detected (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. ![]() We evaluate various categories of deepfakes especially in audio. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. ![]() In some cases, deepfakes can be fabricated using AI-generated content in its entirety. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. ![]() Department of Information System, University of Maryland Baltimore County, Baltimore, MD, United StatesĪ deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis.Zahra Khanjani, Gabrielle Watson and Vandana P. ![]()
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