The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Blind source camera identification
Abstract
An interesting problem in digital forensics is that given a digital image, would it be possible to identify the camera model which was used to obtain the image. In this paper we look at a simplified version of this problem by trying to distinguish between images captured by a limited number of camera models. We propose a number of features which could be used by a classifier to identify the source camera of an image in a blind manner. We also provide experimental results and show reasonable accuracy in distinguishing images from the two and five different camera models using the proposed features.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Statistical evaluation of image quality measures
Sankur, B. (2002) · Journal of Electronic Imaging · doi:10.1117/1.1455011 · Cited by 668
Steganalysis using image quality metrics
Avcibas, I., Memon, N., & Sankur, B. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.807363 · Cited by 562
Self-citationDetecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
Lyu, S., & Farid, H. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-36415-3_22 · Cited by 450
Color processing in digital cameras
Adams, J., Parulski, K., & Spaulding, K. (1998) · IEEE Micro · doi:10.1109/40.743681 · Cited by 233
Methods for identification of images acquired with digital cameras:
Geradts, Z. J., Bijhold, J., Kieft, M., Kurosawa, K., Kuroki, K., & Saitoh, N. (2001) · SPIE Proceedings · doi:10.1117/12.417569 · Cited by 170
Cited by
Digital Camera Identification From Sensor Pattern Noise
Luka, J., Fridrich, J., & Goljan, M. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.873602 · Cited by 1,328
Determining Image Origin and Integrity Using Sensor Noise
Chen, M., Fridrich, J., Goljan, M., & Lukas, J. (2008) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2007.916285 · Cited by 893
Exposing digital forgeries in color filter array interpolated images
Popescu, A. C., & Farid, H. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.855406 · Cited by 656
Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
Hsu, Y.-F., & Chang, S.-F. (2006) · 2006 IEEE International Conference on Multimedia and Expo · doi:10.1109/icme.2006.262447 · Cited by 434
The 'Dresden Image Database' for benchmarking digital image forensics
Gloe, T., & Böhme, R. (2010) · Proceedings of the 2010 ACM Symposium on Applied Computing · doi:10.1145/1774088.1774427 · Cited by 388
Metadata
- Authors
- Kharrazi, M., Sencar, H. T., & Memon, N.
- Venue
- 2004 International Conference on Image Processing, 2004. ICIP '04.
- Publisher
- IEEE
- Published
- 2005-04-19
- Volume
- 1
- Pages
- 709-712
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W1506864492
- Cited by
- 318
- References
- 7
- FWCI
- 13.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kharrazi, M., Sencar, H. T., & Memon, N. (2005). Blind source camera identification. 2004 International Conference on Image Processing, 2004. ICIP '04. https://doi.org/10.1109/icip.2004.1418853
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