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Conference Paper: Source camera identification using footprints from lens aberration
Title | Source camera identification using footprints from lens aberration |
---|---|
Authors | |
Keywords | Forensic science Image processing Lens aberration Source camera identification Statistical classification |
Issue Date | 2006 |
Publisher | S P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml |
Citation | Proceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6069 How to Cite? |
Abstract | Source camera identification is the process of discerning which camera has been used to capture a particular image. In this paper, we consider the more fundamental problem of trying to classify images captured by a limited number of camera models. Inspired by the previous work that uses sensor imperfection, we propose to use the intrinsic lens aberration as features in the classification. In particular, we focus on lens radial distortion as the primary distinctive feature. For each image under investigation, parameters from pixel intensities and aberration measurements are obtained. We then employ a classifier to identify the source camera of an image. Simulation is carried out to evaluate the success rate of our method. The results show that this is a viable procedure in source camera identification with a high probability of accuracy. Comparing with the procedures using only image intensities, our approach improves the accuracy from 87% to 91%. © 2006 SPIE-IS&T. |
Persistent Identifier | http://hdl.handle.net/10722/99555 |
ISSN | 2023 SCImago Journal Rankings: 0.152 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, KS | en_HK |
dc.contributor.author | Lam, EY | en_HK |
dc.contributor.author | Wong, KKY | en_HK |
dc.date.accessioned | 2010-09-25T18:35:11Z | - |
dc.date.available | 2010-09-25T18:35:11Z | - |
dc.date.issued | 2006 | en_HK |
dc.identifier.citation | Proceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6069 | en_HK |
dc.identifier.issn | 0277-786X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/99555 | - |
dc.description.abstract | Source camera identification is the process of discerning which camera has been used to capture a particular image. In this paper, we consider the more fundamental problem of trying to classify images captured by a limited number of camera models. Inspired by the previous work that uses sensor imperfection, we propose to use the intrinsic lens aberration as features in the classification. In particular, we focus on lens radial distortion as the primary distinctive feature. For each image under investigation, parameters from pixel intensities and aberration measurements are obtained. We then employ a classifier to identify the source camera of an image. Simulation is carried out to evaluate the success rate of our method. The results show that this is a viable procedure in source camera identification with a high probability of accuracy. Comparing with the procedures using only image intensities, our approach improves the accuracy from 87% to 91%. © 2006 SPIE-IS&T. | en_HK |
dc.language | eng | en_HK |
dc.publisher | S P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml | en_HK |
dc.relation.ispartof | Proceedings of SPIE - The International Society for Optical Engineering | en_HK |
dc.subject | Forensic science | en_HK |
dc.subject | Image processing | en_HK |
dc.subject | Lens aberration | en_HK |
dc.subject | Source camera identification | en_HK |
dc.subject | Statistical classification | en_HK |
dc.title | Source camera identification using footprints from lens aberration | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Lam, EY:elam@eee.hku.hk | en_HK |
dc.identifier.email | Wong, KKY:kywong@eee.hku.hk | en_HK |
dc.identifier.authority | Lam, EY=rp00131 | en_HK |
dc.identifier.authority | Wong, KKY=rp00189 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1117/12.649775 | en_HK |
dc.identifier.scopus | eid_2-s2.0-33645696767 | en_HK |
dc.identifier.hkuros | 116323 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33645696767&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 6069 | en_HK |
dc.identifier.spage | 155 | en_HK |
dc.identifier.epage | 162 | en_HK |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Choi, KS=12804359900 | en_HK |
dc.identifier.scopusauthorid | Lam, EY=7102890004 | en_HK |
dc.identifier.scopusauthorid | Wong, KKY=36456599700 | en_HK |
dc.identifier.issnl | 0277-786X | - |