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Large scale test of sensor fingerprint camera identification
Abstract
This paper presents a large scale test of camera identification from sensor fingerprints. To overcome the problem of acquiring a large number of cameras and taking the images, we utilized Flickr, an existing on-line image sharing site. In our experiment, we tested over one million images spanning 6896 individual cameras covering 150 models. The gathered data provides practical estimates of false acceptance and false rejection rates, giving us the opportunity to compare the experimental data with theoretical estimates. We also test images against a database of fingerprints, simulating thus the situation when a forensic analyst wants to find if a given image belongs to a database of already known cameras. The experimental results set a lower bound on the performance and reveal several interesting new facts about camera fingerprints and their impact on error analysis in practice. We believe that this study will be a valuable reference for forensic investigators in their effort to use this method in court.
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Cited by
An Evaluation of Popular Copy-Move Forgery Detection Approaches
Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2218597 · Cited by 802
RAISE: a raw images dataset for digital image forensics
Dang-Nguyen, D.-T., Pasquini, C., Conotter, V., & Boato, G. (2015) · Proceedings of the 6th ACM Multimedia Systems Conference · doi:10.1145/2713168.2713194 · Cited by 508
MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation
Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A. N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., & Fiscus, J. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00018 · Cited by 329
Metadata
- Authors
- Goljan, M., Fridrich, J., & Filler, T.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2009-02-05
- Volume
- 7254
- Pages
- 72540I
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2141701079
- Cited by
- 252
- References
- 0
- FWCI
- 10.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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