Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery
This work addresses image forensics for detecting forgeries, but it is incremental as it builds on existing noise modeling and MRF methods.
The paper tackled the problem of blind localization of image splicing forgery by modeling real sensor noise as signal-dependent noise and applying it within a maximum a posterior Markov random field framework, achieving comparative localization performance in experiments.
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.