An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection

Abstract

A novel image copy-move forgery detection scheme using a Dense-InceptionNet is proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense-feature connection, Deep Neural Network (DNN). It is the first DNN model to autonomously learn the feature correlations and search the possible forgery snippets through the matching clues. The proposed Dense-InceptionNet consists of Pyramid Feature Extractor (PFE), Feature Correlation Matching (FCM), and Hierarchical Post-Processing (HPP) modules. The PFE module is proposed to extract multi-dimensional and multi-scale dense-features. The features of each layer in this extractor module are directly connected to the preceding layers. The FCM module is proposed to learn the high correlations of deep features and obtain three candidate matching maps. Finally, the HPP module which makes use of three matching maps to obtain a combination of cross-entropies is amenable to better training via backpropagation. Experiments demonstrate that the efficiency of the proposed Dense-InceptionNet is much better than the other state-of-the-art methods while achieving the relative best performance against most known attacks.