Detecting Image Splicing Based on Noise Level Inconsistency

Abstract

In a spliced image, areas from different origins contain different noise features, which may be exploited as evidence for forgery detection. In this paper, we propose a noise level evaluation method for digital photos, and use the method to detect image splicing. Unlike most noise-based forensic techniques in which an AWGN model is assumed, the noise distribution used in the present work is intensity-dependent. This model can be described with a noise level function (NLF) that better fits the actual noise characteristics. NLF reveals variation in the standard deviation of noise with respect to image intensity. In contrast to denoising problems, noise in forensic applications is generally weak and content-related, and estimation of noise characteristics must be done in small areas. By exploring the relationship between NLF and the camera response function (CRF), we fit the NLF curve under the CRF constraints. We then formulate a Bayesian maximum a posteriori (MAP) framework to optimize the NLF estimation, and develop a method for image splicing detection according to noise level inconsistency in image blocks taking from different origins. Experimental results are presented to show effectiveness of the proposed method.