A new method estimating linear gaussian filter kernel by image PRNU noise

Abstract

In the paper, we present a method estimating the linear Gaussian filter kernel by means of photo-response non-uniformity (PRNU) noise. In the past decade, a large number of works have proved that PRNU noise is a reliable fingerprint for digital image. Accordingly, PRNU noise has been widely used in such fields as camera identification and image manipulation detection. Specifically, in order to detect whether the query image is a Gaussian filtered image and estimate the filter kernel of the Gaussian filtered image, the proposed paper takes fully the camera reference PRNU noise as the identification fingerprint, and we correlate the identification fingerprint with the noise residual extracted from clean image and noise residual extracted from filtered image, respectively. Mathematically, the linear correlation between the two cross-correlation can be determined. In addition, due to the unknown of clean image, how to access to a clean image without additive noise is crucial in this application. Consequently, we propose to use the method of recapturing the query image instead of randomly shooting a large number of content-irrelevant images. It exhibits better estimation result compared to the existing techniques. Moreover, we observe that the proposed method is robust to JPEG compression.