Robust Camera Model Identification Based on Richer Convolutional Feature Network

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

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

Abstract

Based on convolutional neural network (CNN), the problem of robust patch level camera model identification is studied in this paper. Firstly, an effective feature representation is proposed by concatenating a multiscale residual prediction module as well as the original RGB images. Motivated by exploration of multi-scale characteristic, the multiscale residual prediction module automatically learn the residual images to avoid the subsequent CNN being affected by the scene content. Color channel information is integrated for enhanced diversity of CNN inputs. Secondly, a modified richer convolutional feature network is presented for robust camera model identification by fully exploiting the learnt features. Finally, the effectiveness of the proposed method is verified by abundant experimental results at the patch level, which is more difficult than image level experiments.
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

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