Open AccessFace Image Manipulation Detection
Author(s) -
Lilong Wen,
Dan Xu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/533/1/012054
Subject(s) - artificial intelligence , computer science , convolutional neural network , swap (finance) , autoencoder , pattern recognition (psychology) , face (sociological concept) , computer vision , image (mathematics) , network architecture , artificial neural network , social science , computer security , finance , sociology , economics
This paper proposes a CNN-based (Convolutional Neural Network based) network to detect altered face picture, which can cover the most common face swap methods. The network uses an autoencoder which is pre-trained on the original images to reconstruct the input images. The reconstructed one and the input image are then processed by the SRM filter which can extract the noise distribution of images. We then feed the minus result of two processed results into a CNN architecture to predict whether the input image is original or tampered. The model was trained and evaluated in FaceForensics dataset and state-of-art face swap method. Experimental results demonstrate the effectiveness of our network.