Abstract
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Workshop on Machine Learning for Signal Processing |
| Subtitle of host publication | Proceedings of MLSP 2015 |
| Editors | Deniz Erdoğmuş , Murat Akçakaya , Serdar Kozat, Jan Larsen |
| Publisher | IEEE Computer Society |
| Pages | 1-5 |
| Number of pages | 5 |
| Volume | 2015-November |
| ISBN (Electronic) | 9781467374545 |
| DOIs | |
| Publication status | Published - 10 Nov 2015 |
| Event | 25th IEEE International Workshop on Machine Learning for Signal Processing: MLSP 2015 - Northeastern University, Boston, United States Duration: 17 Sep 2015 → 20 Sep 2015 http://mlsp2015.conwiz.dk/home.htm (Conference website) |
Conference
| Conference | 25th IEEE International Workshop on Machine Learning for Signal Processing |
|---|---|
| Country | United States |
| City | Boston |
| Period | 17/09/15 → 20/09/15 |
| Internet address |
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Random subspace method for source camera identification. / Li, Ruizhe; Kotropoulos, Constantine; Li, Chang Tsun; Guan, Yu.
2015 IEEE International Workshop on Machine Learning for Signal Processing: Proceedings of MLSP 2015. ed. / Deniz Erdoğmuş ; Murat Akçakaya ; Serdar Kozat; Jan Larsen. Vol. 2015-November IEEE Computer Society, 2015. p. 1-5 7324339.Research output: Book chapter/Published conference paper › Conference paper
TY - GEN
T1 - Random subspace method for source camera identification
AU - Li, Ruizhe
AU - Kotropoulos, Constantine
AU - Li, Chang Tsun
AU - Guan, Yu
PY - 2015/11/10
Y1 - 2015/11/10
N2 - Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve.
AB - Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve.
KW - Digital forensics
KW - PCA denoising
KW - Random subspace method
KW - Sensor pattern noise
UR - http://www.scopus.com/inward/record.url?scp=84960863809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960863809&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2015.7324339
DO - 10.1109/MLSP.2015.7324339
M3 - Conference paper
AN - SCOPUS:84960863809
VL - 2015-November
SP - 1
EP - 5
BT - 2015 IEEE International Workshop on Machine Learning for Signal Processing
A2 - Erdoğmuş , Deniz
A2 - Akçakaya , Murat
A2 - Kozat, Serdar
A2 - Larsen, Jan
PB - IEEE Computer Society
ER -