Fake Image Detection Using Machine Learning

Abstract: Nowadays biometric systems are useful in recognizing a person’s identity, but criminals change their appearance in behaviour and psychological to deceive recognition system. To overcome this problem we are using a new technique called Deep Texture Features extraction from images and then building train machine learning model using CNN (Convolution Neural Networks) algorithm. This technique refers as LBPNet or NLBPNet as this technique is heavily dependent on features extraction using LBP (Local Binary Pattern) algorithm. In this project, we are designing LBP Based machine learning Convolution Neural Network called LBPNET to detect fake face images. Here first we will extract LBP from images and then train LBP descriptor images with Convolution Neural Network to generate a training model. Whenever we upload a new test image then that test image will be applied to the training model to detect whether the test image contains a fake image or a non-fake image. Below we can see some details on LBP.

Keywords: Biometry, Identity, Recognition, Detection, Fake face.