Detecting Image Tampering Using Feature Fusion

Along with the development of sophisticated image processing software, it is getting easier forging a digital image but harder to detect it. It is already a problem for us to distinguish tampered photos from authentic ones. In this paper, we propose an approach based on feature fusion to detect digital image tampering. First, we extract the feature statistics that can represent the property of a camera from the images taken by that camera. These feature statistics are used for training a one-class classifier in order to get the feature pattern of the given camera. Then, we do sliding segmentation to testing images. Finally, feature statistics extracted from image blocks are fed into the trained one-class classifier to match the feature pattern of the given camera. The images with low percentage of matched blocks are classified as tampered ones. Our method could achieve a high accuracy in detecting the tampered images that undergone post-processing such as JPEG compression, re-sampling and retouching.