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Image forgery detection
Abstract
We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. From the tabloid magazines to the fashion industry and in mainstream media outlets, scientific journals, political campaigns, courtrooms, and the photo hoaxes that land in our e-mail in-boxes, doctored photographs are appearing with a growing frequency and sophistication. Over the past five years, the field of digital forensics has emerged to help restore some trust to digital images. The author reviews the state of the art in this new and exciting field.
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Cited by
MesoNet: a Compact Facial Video Forgery Detection Network
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630761 · Cited by 1,872
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237
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
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
Generalizing Face Forgery Detection with High-frequency Features
Luo, Y., Zhang, Y., Yan, J., & Liu, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01605 · Cited by 609
Metadata
- Authors
- Farid, H.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2009-03
- Volume
- 26
- Issue
- 2
- Pages
- 16-25
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2155319953
- Cited by
- 925
- References
- 0
- FWCI
- 54.24
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
