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 becomeincreasingly 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 abuseregularly. 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.