  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. This survey has
been conducted with a different perspective compared to existing survey papers,
that mostly focus on just video and image deepfakes. This survey not only
evaluates generation and detection methods in the different deepfake
categories, but mainly focuses on audio deepfakes that are overlooked in most
of the existing surveys. This paper critically analyzes and provides a unique
source of audio deepfake research, mostly ranging from 2016 to 2020. To the
best of our knowledge, this is the first survey focusing on audio deepfakes in
English. This survey provides readers with a summary of 1) different deepfake
categories 2) how they could be created and detected 3) the most recent trends
in this domain and shortcomings in detection methods 4) audio deepfakes, how
they are created and detected in more detail which is the main focus of this
paper. We found that Generative Adversarial Networks(GAN), Convolutional Neural
Networks (CNN), and Deep Neural Networks (DNN) are common ways of creating and
detecting deepfakes. In our evaluation of over 140 methods we found that the
majority of the focus is on video deepfakes and in particular in the generation
of video 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 of heavy overlaps with human generation of fake content. This paper
is an abbreviated version of the full survey and reveals a clear need to
research audio deepfakes and particularly detection of audio deepfakes.
