A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
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Published:2023-05-29
Issue:11
Volume:13
Page:6583
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Li Jian1,
Liu Yang1,
Ma Bin1,
Wang Chunpeng1ORCID,
Qin Chuan2,
Wu Xiaoming3,
Li Shuanshuan1
Affiliation:
1. Department of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
2. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
3. Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Abstract
Photo response non-uniformity (PRNU) is a widely accepted inherent fingerprint that has been used in source camera identification (SCI). However, the reference PRNU noise is limited by the performance of PRNU noise extraction techniques and is easily contaminated by interfering noise from image content. The existing methods mainly suppressed the interference noise of the reference PRNU noise in the spectral domain, but there was still interference noise related to the image content in the low-frequency region. We considered that this interference noise of the reference PRNU noise could be removed by further operations in the spatial domain. In this paper, we proposed a scheme to distil the reference PRNU by removing the interference noise with the help of principal component analysis (PCA) technology. Specifically, the reference PRNU noise was modelled as white Gaussian noise, whereas the interfering noise caused correlation between pixels and their neighbourhoods in the reference PRNU noise. In the local pixel area, we modelled a pixel and its neighbours as a vector and used block matching to select PCA training samples with similar contents. Next, PCA transformation estimated the interference noise in the local pixel area, and we performed coefficient shrinkage in the PCA domain to better estimate interference noise. The experimental results on the “Dresden” and “VISION” datasets showed that the proposed scheme achieved better receiver operating characteristic curves and the Kappa statistic than state-of-the-art works.
Funder
National Natural Science Foundation of China
National key research and development program of China
Shandong Provincial Natural Science Foundation Innovation and Development Joint Fund
Shandong Provincial Natural Science Foundation
Ability Improvement Project of Science and technology SMES in Shandong Province
Project of Jinan Research Leader Studio
Project of Jinan introduction of innovation team
Project of Jinan City-School Integration Development
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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