Digital image forensics of non-uniform deblurring

Abstract

Non-uniform image deblurring has been extensively studied, but forensics of whether an image is non-uniform deblurred is still an untouched area. In this paper, we firstly propose an approach to localize the non-uniform deblurring in digital images. Firstly, taking advantage of the properties of convolution derivation, multi-derivative gray level co-occurrence matrix (MGLCM) features are proposed to reveal the deblurring artifacts of images. The MGLCM features are extracted from the first and the second order derivative of images. Then sliding window strategy is used. For each sliding window, MGLCM features are extracted and SVMs are exploited to score the detection probability. By changing the scale of the sliding windows, a series of detection probability maps at different scales are obtained. Finally, top-down multi-scale boundary fusion (TMBF) is proposed to get the final detection map. The experimental results demonstrate that the proposed approach successfully localize the deblurred regions with a satisfactory performance.