Image Forgery Localization based on Multi-Scale Convolutional Neural Networks

Abstract

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, we adopt a unified CNN architecture. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of tampering detectors based on CNNs for different scales, a series of complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse these maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.