The DOI matches its Crossref record.
Fake Photos
Abstract
A concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Stalin, Mao, Hitler, Mussolini, and other dictators routinely doctored photographs so that the images aligned with their messages. They erased people who were there, added people who were not, and manipulated backgrounds. They knew if they changed the visual record, they could change history. Once, altering images required hours in the darkroom; today, it can be done with a keyboard and mouse. Because photographs are so easily faked, fake photos are everywhere—supermarket tabloids, fashion magazines, political ads, and social media. How can we tell if an image is real or false? In this volume in the MIT Press Essential Knowledge series, Hany Farid offers a concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Farid, an expert in photo forensics, has spent two decades developing techniques for authenticating digital images. These techniques model the entire image-creation process in order to find the digital disruption introduced by manipulation of the image. Each section of the book describes a different technique for analyzing an image, beginning with those requiring minimal technical expertise and advancing to those at intermediate and higher levels. There are techniques for, among other things, reverse image searches, metadata analysis, finding image imperfections introduced by JPEG compression, image cloning, tracing pixel patterns, and detecting images that are computer generated. In each section, Farid describes the techniques, explains when they should be applied, and offers examples of image analysis.
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Traditional digital media forensics often rely on image metadata that are not available for many of today's most concerning deepfakes, which typically appear first on social media platforms stripped of such metadata.
“traditional digital media forensics, which often rely on image metadata (14) that are not available for many of today’s most concerning deepfakes, which typically appear first on social media platforms stripped of such metadata (15, 16).”
From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [14]
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Deepfake detection by human crowds, machines, and machine-informed crowds
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N. A., & Johnson, D. K. (2019) · New Media & Society · doi:10.1177/1461444820925811 · Cited by 235
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194
Think Twice Before Detecting GAN-generated Fake Images from their Spectral Domain Imprints
Dong, C., Kumar, A., & Liu, E. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00771 · Cited by 72
Deepfakes and documentary practice in an age of misinformation
Hight, C. (2021) · Continuum · doi:10.1080/10304312.2021.2003756 · Cited by 23
Advancing Copy-Move Manipulation Detection in Complex Image Scenarios Through Multiscale Detector
Diwan, A., Mahadeva, R., & Gupta, V. (2024) · IEEE Access · doi:10.1109/access.2024.3397466 · Cited by 13
Visual trust: Fake images in the Russia‐Ukraine war
Canals, R. (2022) · Anthropology Today · doi:10.1111/1467-8322.12767 · Cited by 7
Hu, B., & Huang, G. (2025) · Social Media + Society · doi:10.1177/20563051251401815 · Cited by 7
Fake Photos in the European Refugee Crisis
Howarth, A. (2020) · Advances in public policy and administration (APPA) book series · doi:10.4018/978-1-7998-4796-0.ch008 · Cited by 1
Sánchez-Olmos, C., Ferrándiz, R. R., & Hidalgo‐Marí, T. (2024) · Cuardernos de documentación multimedia/Cuadernos de documentación multimedia · doi:10.5209/cdmu.95574 · Cited by 1
Bonnici, L. J., & O’Connor, B. C. (2022) · Synthesis lectures on information concepts, retrieval, and services · doi:10.1007/978-3-031-17094-2_2
Gancitano, M., & Colamedici, A. (2026) · doi:10.3390/proceedings2026139018
Metadata
- Authors
- Farid, H.
- Corresponding author
- Farid, Hany
- ORCID
- Farid, Hany
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2019-09-10
- OpenAlex
- W4244890069
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2022: 3 · 2021: 2 · 2020: 1 · 2019: 1
- FWCI
- 1.00
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- image forgery detection, fake images, digital image authentication, JPEG compression artifacts, image cloning, reverse image search, image forensics
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