Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
This addresses image forgery detection for digital forensics, but appears incremental as it builds on existing CNN methods with multi-scale enhancements.
The paper tackles the problem of localizing tampered areas in digital images by proposing a multi-scale convolutional neural network approach combined with segmentation-based analysis, achieving a performance leap in forgery localization.
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, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the 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.