Fake Image Detection using machine learning is a neural network based project written in Java with JavaFX that helps to identify tampered / faked / photoshopped images.
The objective of this project is to identify fake images(Fake images are the images that are digitally altered images). The problem with existing fake image detection system is that they can be used detect only specific tampering methods like splicing, coloring etc. We approached the problem using machine learning and neural network to detect almost all kinds of tampering on images.
Using latest image editing softwares, it is possible to make alterations on image which are too difficult for human eye to detect. Even with a complex neural network, it is not possible to determine whether an image is fake or not without identifying a common factor across almost all fake images. So, instead of giving direct raw pixels to the neural network, we gave error level analysis image.
Fake image detection projects introduces two different levels of analysis for the image. At first level, it checks the image metadata. Image metadata is not that much reliable since it can be altered using simple programs. But most of the images we come across will have non-altered metadata which helps to identify the alterations. For example, if an image is edited with Adobe Photoshop, the metadata will contain even the version of the Adobe Photoshop used.
A multilayer perceptron neural network is used having one input layer, 3 hidden layers and 1 output layer. Once the image is selected for evaluation, it is converted to ELA representation from Compression and Error Level Analysis stage. 100%, 90% images are used for the construction of ELA image.
Once ELA is calculated, the image is preprocessed to convert into 100x100px width and height. After preprocessing, the image is serialized in to an array. The array contains 30,000 integer values representing 10,000 pixels. Since each pixel has red, green and blue components, 10,000 pixels will have 30,000 values.
During training, the array is given as input to the multilayer perceptron network and output neurons also set. The MLP is a fully connected neural network. There are 2 output neurons. First neuron is for representing fake and the second one for real image. If the given image is fake one, then the fake neuron is set to one and real is set to zero. Else fake is set to zero and real set to one.
We have used momentum backpropagation learning rule adjust the neuron connection weights. It is a supervised learning rule that tries to minimize the error function. The chosen learning rate and momentum along with achieved efficiency is given in Table 3.
During testing, the image array is fed into the input neurons and values of output neurons are taken. We have used sigmoid activation function.