The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Determining Image Origin and Integrity Using Sensor Noise
Abstract
In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.
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Cites
1 retracted
Kennedy, D. (2006) · Science · doi:10.1126/science.1124926 · Cited by 117
RetractedChen, W., Shi, Y. Q., & Su, W. (2007) · SPIE Proceedings · doi:10.1117/12.704321 · Cited by 170
Pearson, H. (2005) · Nature · doi:10.1038/434952a · Cited by 67
Cited by
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499
Information Forensics: An Overview of the First Decade
Stamm, M. C., Wu, M., & Liu, K. J. R. (2013) · IEEE Access · doi:10.1109/access.2013.2260814 · Cited by 394
Do GANs leave artificial fingerprints?
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2019) · 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00103 · Cited by 361
Splicebuster: A new blind image splicing detector
Cozzolino, D., Poggi, G., & Verdoliva, L. (2015) · 2015 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2015.7368565 · Cited by 283
Metadata
- Authors
- Chen, M., Fridrich, J., Goljan, M., & Lukas, J.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2008
- Volume
- 3
- Issue
- 1
- Pages
- 74-90
- ISSN
- 1556-6013 (ISSN-L)
- OpenAlex
- W2096754397
- Cited by
- 893
- References
- 3
- FWCI
- 56.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, M., Fridrich, J., Goljan, M., & Lukas, J. (2008). Determining Image Origin and Integrity Using Sensor Noise. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2007.916285
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
