Comparative Analysis of Deep-Fake Algorithms
Nikhil Sontakke Sejal Utekar Shivansh Rastogi Shriraj Sonawane
Due to the widespread use of smartphones with high-quality digital cameras
and easy access to a wide range of software apps for recording, editing, and
sharing videos and images, as well as the deep learning AI platforms, a new
phenomenon of 'faking' videos has emerged. Deepfake algorithms can create fake
images and videos that are virtually indistinguishable from authentic ones.
Therefore, technologies that can detect and assess the integrity of digital
visual media are crucial. Deepfakes, also known as deep learning-based fake
videos, have become a major concern in recent years due to their ability to
manipulate and alter images and videos in a way that is virtually
indistinguishable from the original. These deepfake videos can be used for
malicious purposes such as spreading misinformation, impersonating individuals,
and creating fake news. Deepfake detection technologies use various approaches
such as facial recognition, motion analysis, and audio-visual synchronization
to identify and flag fake videos. However, the rapid advancement of deepfake
technologies has made it increasingly difficult to detect these videos with
high accuracy. In this paper, we aim to provide a comprehensive review of the
current state of deepfake creation and detection technologies. We examine the
various deep learning-based approaches used for creating deepfakes, as well as
the techniques used for detecting them. Additionally, we analyze the
limitations and challenges of current deepfake detection methods and discuss
future research directions in this field. Overall, the paper highlights the
importance of continued research and development in deepfake detection
technologies in order to combat the negative impact of deepfakes on society and
ensure the integrity of digital visual media.