Detecting Image Forgery Based on Noise Estimation

With the advent of the Internet and low-price digital cameras, as well as powerful image editing software, the authenticity of digital images can no longer be taken for granted. Image noises are often introduced into the tampered region during image manipulation process. In this paper, we propose a detection method to locate image forgeries based on noise estimation on HSV color space and hybrid clustering method combined with unsupervised clustering and supervised clustering. A suspicious image is first converted into HSV color space and segmented into non-overlapping image blocks. Then the noise variance at each local image block is estimated as input of unsupervised clustering. Finally, a supervised clustering method based on SVM is used to further improve the detection accuracy. Our experimental results demonstrate that the proposed method can effectively expose tampered regions from tampered images.