Fake image detection using machine learning
Date
2018-03-23
Authors
Jama, Ahmed, Mohamoud
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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.
Description
A research report submitted in partial fulfilment of the requirement for the degree of Master of Science in Computer Science to the Faculty of Science, University of the Witwatersrand, 2018
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Citation
Jama, Ahmed Mohamoud, (2018) Fake image detection using machine learning, University of the Witwatersrand, Johannesburg, https://hdl.handle.net/10539/27007