  Easy access to audio-visual content on social media, combined with the
availability of modern tools such as Tensorflow or Keras, open-source trained
models, and economical computing infrastructure, and the rapid evolution of
deep-learning (DL) methods, especially Generative Adversarial Networks (GAN),
have made it possible to generate deepfakes to disseminate disinformation,
revenge porn, financial frauds, hoaxes, and to disrupt government functioning.
The existing surveys have mainly focused on deepfake video detection only. No
attempt has been made to review approaches for detection and generation of both
audio and video deepfakes. This paper provides a comprehensive review and
detailed analysis of existing tools and machine learning (ML) based approaches
for deepfake generation and the methodologies used to detect such manipulations
for the detection and generation of both audio and video deepfakes. For each
category of deepfake, we discuss information related to manipulation
approaches, current public datasets, and key standards for the performance
evaluation of deepfake detection techniques along with their results.
Additionally, we also discuss open challenges and enumerate future directions
to guide future researchers on issues that need to be considered to improve the
domains of both the deepfake generation and detection. This work is expected to
assist the readers in understanding the creation and detection mechanisms of
deepfake, along with their current limitations and future direction.
