Effective Source Camera Identification based on MSEPLL Denoising Applied to Small Image Patches

Wen-Na Zhang, Yun-Xia Liu, Ze-Yu Zou, Yun-Li Zang, Yang Yang, Ngai Fong Law

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Sensor Pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification, while its estimation accuracy heavily relies on denoising algorithm. In this paper, an effective source camera identification scheme based on Multi-Scale Expected Patch Log Likelihood (MSEPLL) denoising algorithm is proposed, firstly. With enhanced prior modeling across multiple scales, MSEPLL can accurately restore the original image. As a consequence, estimated SPN is less influenced by image content. Secondly, the source camera identification problem is formulated by hypothesis testing, where normalized correlation coefficient is adopted for SPN detection. Finally, the effectiveness of the proposed method is verified by abundant experiments in terms of identification accuracy as well as receiver operating characteristic. Performance improvement is more prominent for small image patches, which is more conducive to real forensics applications.
Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
DOIs
Publication statusPublished - Nov 2019
Event2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
Duration: 18 Nov 201921 Nov 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Country/TerritoryChina
CityLanzhou
Period18/11/1921/11/19

Cite this