Fake image detection using machine learning

Abstract

The rapid growth of digital image processing technologies and editing software has given rise to large amounts of tampered images circulating in our daily lives. This undermines credibility and trustworthiness of digital images and also creates false beliefs in many real world situations. Hence it is generating a great demand for automatic forgery detection algorithms in order to determine the authenticity of a candidate image in a timely fashion. Many techniques and tools have been implemented to detect such type of forgery with the real image but because of increasing editing software every day, the problem is not solved yet. In this thesis, we will first introduce some information about digital photographs, their formats and how they are compressed, we will also introduce some important tools that were implemented to detect image forgery and how well these tools can detect faked or tampered images. In this work we wish to test whether a convolutional neural network can be trained to efficiently detect image forgeries. The CNN will be trained first using two classes (real and fake), and then using the same CNN architecture with three classes (real, copy-paste and spliced images). The network will be trained and tested using the standard Casia 2:0 dataset. Then we will compare the convolutional neural network that we trained with some of the implemented tools that classifies images into real and fake. We observe a very good performance after the training. That means the trained CNN network is able to recognize the tampered images at a maximum success rate of 85:63%. So the use of this application can be used as a false proof technique in digital authentication. It will also greatly reduce spreading of fake images through websites and through social media.