Random subspace method for source camera identification

Ruizhe Li, Constantine Kotropoulos, Chang Tsun Li, Yu Guan

Research output: Book chapter/Published conference paperConference paper

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing
Subtitle of host publicationProceedings of MLSP 2015
EditorsDeniz Erdoğmuş , Murat Akçakaya , Serdar Kozat, Jan Larsen
PublisherIEEE Computer Society
Pages1-5
Number of pages5
Volume2015-November
ISBN (Electronic)9781467374545
DOIs
Publication statusPublished - 10 Nov 2015
Event25th IEEE International Workshop on Machine Learning for Signal Processing: MLSP 2015 - Northeastern University, Boston, United States
Duration: 17 Sep 201520 Sep 2015
http://mlsp2015.conwiz.dk/home.htm (Conference website)

Conference

Conference25th IEEE International Workshop on Machine Learning for Signal Processing
CountryUnited States
CityBoston
Period17/09/1520/09/15
Internet address

Fingerprint

Cameras
Image classification
Principal component analysis
Feature extraction
Imaging techniques
Sensors

Cite this

Li, R., Kotropoulos, C., Li, C. T., & Guan, Y. (2015). Random subspace method for source camera identification. In D. Erdoğmuş , M. Akçakaya , S. Kozat, & J. Larsen (Eds.), 2015 IEEE International Workshop on Machine Learning for Signal Processing: Proceedings of MLSP 2015 (Vol. 2015-November, pp. 1-5). [7324339] IEEE Computer Society. https://doi.org/10.1109/MLSP.2015.7324339
Li, Ruizhe ; Kotropoulos, Constantine ; Li, Chang Tsun ; Guan, Yu. / Random subspace method for source camera identification. 2015 IEEE International Workshop on Machine Learning for Signal Processing: Proceedings of MLSP 2015. editor / Deniz Erdoğmuş ; Murat Akçakaya ; Serdar Kozat ; Jan Larsen. Vol. 2015-November IEEE Computer Society, 2015. pp. 1-5
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abstract = "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.",
keywords = "Digital forensics, PCA denoising, Random subspace method, Sensor pattern noise",
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Li, R, Kotropoulos, C, Li, CT & Guan, Y 2015, Random subspace method for source camera identification. in D Erdoğmuş , M Akçakaya , S Kozat & J Larsen (eds), 2015 IEEE International Workshop on Machine Learning for Signal Processing: Proceedings of MLSP 2015. vol. 2015-November, 7324339, IEEE Computer Society, pp. 1-5, 25th IEEE International Workshop on Machine Learning for Signal Processing, Boston, United States, 17/09/15. https://doi.org/10.1109/MLSP.2015.7324339

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 paperConference paper

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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

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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.

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Li R, Kotropoulos C, Li CT, Guan Y. Random subspace method for source camera identification. In Erdoğmuş D, Akçakaya M, Kozat S, Larsen J, editors, 2015 IEEE International Workshop on Machine Learning for Signal Processing: Proceedings of MLSP 2015. Vol. 2015-November. IEEE Computer Society. 2015. p. 1-5. 7324339 https://doi.org/10.1109/MLSP.2015.7324339