  Visual noise is often regarded as a disturbance in image quality, whereas it
can also provide a crucial clue for image-based forensic tasks. Conventionally,
noise is assumed to comprise an additive Gaussian model to be estimated and
then used to reveal anomalies. However, for real sensor noise, it should be
modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing
forgery localization tasks. Through statistical analysis of the SDN model, we
assume that noise can be modeled as a Gaussian approximation for a certain
brightness and propose a likelihood model for a noise level function. By
building a maximum a posterior Markov random field (MAP-MRF) framework, we
exploit the likelihood of noise to reveal the alien region of spliced objects,
with a probability combination refinement strategy. To ensure a completely
blind detection, an iterative alternating method is adopted to estimate the MRF
parameters. Experimental results demonstrate that our method is effective and
provides a comparative localization performance.
