Exposing image splicing with inconsistent local noise variances

Abstract

Image splicing is a simple and common image tampering operation, where a selected region from an image is pasted into another image with the aim to change its content. In this paper, based on the fact that images from different origins tend to have different amount of noise introduced by the sensors or post-processing steps, we describe an effective method to expose image splicing by detecting inconsistencies in local noise variances. Our method estimates local noise variances based on an observation that kurtosis values of natural images in band-pass filtered domains tend to concentrate around a constant value, and is accelerated by the use of integral image. We demonstrate the efficacy and robustness of our method based on several sets of forged images generated with image splicing.