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Determining digital image origin using sensor imperfections
Abstract
In this paper, we demonstrate that it is possible to use the sensor’s pattern noise for digital camera identification from images. The pattern noise is extracted from the images using a wavelet-based denoising filter. For each camera under investigation, we first determine its reference noise, which serves as a unique identification fingerprint. This could be done using the process of flat-fielding, if we have the camera in possession, or by averaging the noise obtained from multiple images, which is the option taken in this paper. To identify the camera from a given image, we consider the reference pattern noise as a high-frequency spread spectrum watermark, whose presence in the image is established using a correlation detector. Using this approach, we were able to identify the correct camera out of 9 cameras without a single misclassification for several hundred images. Furthermore, it is possible to perform reliable identification even from images that underwent subsequent JPEG compression and/or resizing. These claims are supported by experiments on 9 different cameras including two cameras of exactly same model (Olympus C765).
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Cited by
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
Exposing digital forgeries by detecting inconsistencies in lighting
Johnson, M. K., & Farid, H. (2005) · Proceedings of the 7th workshop on Multimedia and security · doi:10.1145/1073170.1073171 · Cited by 406
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
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383
Image acquisition forensics: Forensic analysis to identify imaging source
McKay, C., Swaminathan, A., Gou, H., & Wu, M. (2008) · 2008 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2008.4517945 · Cited by 62
Metadata
- Authors
- Lukas, J., Fridrich, J., & Goljan, M.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2005-03-14
- Volume
- 5685
- Pages
- 249
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W1988600563
- Cited by
- 175
- References
- 0
- FWCI
- 10.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lukas, J., Fridrich, J., & Goljan, M. (2005). Determining digital image origin using sensor imperfections. SPIE Proceedings. https://doi.org/10.1117/12.587105
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