The Library
Inference of a compact representation of sensor fingerprint for source camera identification
Tools
Tools
Tools
Li, Ruizhe, Li, Chang-Tsun and Guan, Yu (2018) Inference of a compact representation of sensor fingerprint for source camera identification. Pattern Recognition, 74 (2). pp. 556-567. doi:10.1016/j.patcog.2017.09.027 ISSN 0031-3203.
|
PDF
WRAP-inference-compact-representation-sensor-fingerprint-source-camera-identification-Li-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1525Kb) | Preview |
|
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-inference-compact-representation-sensor-fingerprint-camera-Li-2018.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2139Kb) |
Official URL: https://doi.org/10.1016/j.patcog.2017.09.027
Abstract
Sensor pattern noise (SPN) is an inherent fingerprint of imaging devices, which provides an effective way for source camera identification (SCI). Although SPNs extracted from large image blocks usually yield high identification accuracy, their high dimensionality would incur a high computational cost in the matching stage, consequently hindering many applications that require efficient camera matchings. In this work, we employ and evaluate the concept of principal component analysis (PCA) de-noising in SCI tasks. Based on this concept, we present a framework that formulates a compact SPN representation. To enhance the de-noising effect, we introduce a training set construction procedure that minimizes the impact of various interfering artifacts, which is especially useful in some challenging cases, e.g., when only textured reference images are available. To further boost the SCI performance, a novel approach based on linear discriminant analysis (LDA) is adopted to extract more discriminant SPN features. To evaluate our methods, extensive experiments are conducted on the Dresden image database. The results indicate that the proposed framework can serve as an effective post-processing procedure, which not only boosts the performance, but also greatly reduces the computational cost in the matching phase.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HV Social pathology. Social and public welfare T Technology > TR Photography |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Forensic sciences, Legal photography, Cameras -- Identification -- Mathematical models | ||||||||
| Journal or Publication Title: | Pattern Recognition | ||||||||
| Publisher: | Pergamon | ||||||||
| ISSN: | 0031-3203 | ||||||||
| Official Date: | 1 February 2018 | ||||||||
| Dates: |
|
||||||||
| Volume: | 74 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 556-567 | ||||||||
| DOI: | 10.1016/j.patcog.2017.09.027 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Copyright Holders: | Elsevier | ||||||||
| Date of first compliant deposit: | 19 January 2018 | ||||||||
| Date of first compliant Open Access: | 28 September 2018 | ||||||||
| Funder: | Commission of the European Communities | ||||||||
| RIOXX Funder/Project Grant: |
|
||||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
