Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery

• URL: http://arxiv.org/abs/2010.16211v2
• Date: Mon, 2 Nov 2020 09:34:16 GMT
• Title: Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery
• Authors: Mian Zou, Heng Yao, Chuan Qin, and Xinpeng Zhang
• Abstract summary: In this work, we apply signal-dependent noise (SDN) to splicing localization tasks. 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. Experimental results demonstrate that our method is effective and provides a comparative localization performance.
• Score: 20.533239616846874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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