Deep learning-based image forgery detection

Abstract

In recent years, different kinds of image synthesis models have been developed, which are widely used in the field of deep learning and daily life. However, many people are troubled by these techniques because they can be utilized by malicious people to produce fake images. Nevertheless, different image synthesis techniques emerge one after another, and few existing image discrimination models can detect these different types of images. This dissertation discusses a model that can detect different image synthesis techniques, which has good practical application and research value. In addition, we introduce a brand new dataset, TMCface. We use this dataset and other public datasets to compare the performance of baselines with ours. Keywords: Image forensics, Generalization, DeepLearning, ResNet50, TMCface.