Document forgery detection based on spatial-frequency and multi-scale feature network

Abstract

Passive image forgery detection is one of the main tasks for digital image forensics. Although it is easy to detect and localize forged regions with high accuracies from tampered images through utilizing the diversity and rich detail features of natural images, detecting tampered regions from a tampered textual document image (photographs) still presents many challenges. These challenges include poor detection results and difficulty of identifying the applied forgery type. In this paper, we propose a robust multi-category tampering detection algorithm based on spatial-frequency(SF) domain and multi-scale feature fusion network. First, we employ frequency domain transform and SF feature fusion strategy to strengthen the network’s ability to discriminate tampered document textures. Secondly, we combine HRNet, attention mechanism and a multi-supervision module to capture the features of the document images at different scales and improve forgery detection results. Furthermore, we design a multi-category detection head module to detect multiple types of forgeries that can improve the generalization ability of the proposed algorithm. Extensive experiments on a constructed dataset based on the public StaVer and SCUT-EnsExam datasets have been conducted. The experimental results show that the proposed algorithm improves F1 score of document images tampering detection by nearly 5.73%, and it’s not only able to localize the tampering location, but also accurately identify the applied tampering type.