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Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency
Abstract
Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image
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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
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
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
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
- Hsu, Y.-F., & Chang, S.-F.
- Venue
- 2006 IEEE International Conference on Multimedia and Expo
- Publisher
- IEEE
- Published
- 2006-07
- Pages
- 549-552
- OpenAlex
- W2104116677
- Cited by
- 434
- References
- 0
- FWCI
- 5.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hsu, Y.-F., & Chang, S.-F. (2006). Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency. 2006 IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/icme.2006.262447
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