Survey on Detection of Manipulated Multimedia in Digital Forensics Using Machine Learning

Abstract

The manipulation of multimedia has
increased all over the world. Different tools
are used to alter the multimedia and it is
difficult to detect genuine and fake media.
People are facing problems to detect if the
media is real or fake. Due to manipulated
media, cybercrime has become increasingly
widespread. We believe that personal security
and privacy should be carried out easily and
intelligently in this digital environment where
all fundamental tasks are completed without
issue. When we looked into the numbers, we
discovered that a sizable proportion of people
experience harassment or other forms of
abuse regularly. Based on a review of the
existing system, we presented an application
that would use the CNN (Convolutional
Neural Networks) method to distinguish
between real and fraudulent media in a single
application. CNN performs better with
picture and voice or audio inputs than earlier
networks and other techniques. CNN hidden
extract feature from the input using pixels
value and computation based on edges and
outline of the inputs using pixels value and
computation based on edges and outline of the
inputs. The growing use of convolutional
neural networks (CNNs) has had a substantial
effect on defenders.