MVFNN: Multi-Vision Fusion Neural Network for Fake News Picture Detection

ABSTRACT

During this year’s Novel Coronavirus (2019-nCoV) outbreak, the spread of fake news has caused serious social panic. This fact necessitates a focus on fake news detection. Pictures could be viewed as fake news indicators and hence could be used to identify fake news effectively. However, fake news pictures detection is more challenging since fake news picture identification is more difficult than the fake picture recognition. This paper proposes a multi-vision fusion neural network (MVFNN) which consists of four main components the visual modal module, the visual feature fusion module, the physical feature module and the ensemble module. The visual modal module is responsible for extracting image features from images pixel domain, frequency domain, and tamper detection. It cooperates with the visual features fusion module to detect fake news images from multi-vision fusion. And the ensemble module combines visual features and physical features to detect the fake news pictures. Experimental results show that our model could achieve better detection performance by at least 4.29% than the existing methods in benchmark datasets. © 2020, Springer Nature Switzerland AG.