The DOI matches its Crossref record.
Deepfake Deception: What to Trust When Seeing Is No Longer Believing
Abstract
The deep learning generative adversarial networks (GANs) discussed in the previous chapter for creating synthetic photos have also been applied to video synthesis and editing. Clips can now be created of people doing and saying things they never did or said in real life. This is leading to a double-pronged challenge in society’s attempts at discerning the truth: fake videos are spreading across the internet causing people to believe in events that never took place, and simultaneously real videos have been falsely claimed as deepfakes causing people to doubt reality itself. In this chapter, you will see how deepfake videos have impacted politics and journalism, how the discord they sow relates to that of previous generations of image and video manipulation, and what legal and technological attempts are being made to rein them in.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The use of synthetic media in marketplace deception differs from traditional deception in several ways (Giansiracusa, 2021; Karnouskos, 2020; Mechner, 2010; Mirsky & Lee, 2021; Van Huynh et al., 2021).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“Thus, the recent introduction of deepfakes makes marketplace deception even more damaging, as hyper-realistic videos and other multimedia deepfakes are extremely difficult to differentiate from reality (Boush et al., 2015; Giansiracusa, 2021; Tahir et al., 2021; Zhao et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“Firms can be viciously harmed (e.g., through reputation loss) by adversary-initiated deepfake propagation (Botha & Pieterse, 2020; Giansiracusa, 2021; Zakrzewski, 2019).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“For instance, the dubbing and re-voicing industry, which previously translated films to ensure words in another language matched the actor's original lip movements, is at risk of becoming extinct due to the advancing technological ability to change languages and lips accordingly (Giansiracusa, 2021; Johnson & Diakopoulos, 2021; Zakrzewski, 2019).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“From a commercial standpoint, deepfake technology has the potential to increase uncertainty in the marketplace and mislead consumers, resulting in their mistrust of businesses and psychological discomfort (Botha & Pieterse, 2020; Giansiracusa, 2021; Zakrzewski, 2019).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“Moreover, most studies have perceived deepfakes as a grave danger (e.g., Giansiracusa, 2021; Graham et al., 2021; Maksutov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
In the citing paper
“Furthermore, the technology may appear mystical and incomprehensible to the regular person with a non-technical background, eliciting responses of intimidation and fear (Giansiracusa, 2021; Graham et al., 2021; Wagner & Blewer, 2019).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Giansiracusa, 2021
Citations
Cited by
Sorted by citations
Deepfakes: Deceptions, mitigations, and opportunities
Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113368 · Cited by 209
Metadata
- Authors
- Giansiracusa, N.
- Corresponding author
- Giansiracusa, Noah
- ORCID
- Giansiracusa, Noah
- Venue
- How Algorithms Create and Prevent Fake News
- Publisher
- Apress
- Published
- 2021
- Pages
- 41-66
- OpenAlex
- W3178376411
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 2
- FWCI
- 1.81
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Apress eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfakes, deepfake videos, generative adversarial networks, video generation, video editing, misinformation, fake news, political manipulation, journalism, video manipulation, deepfake detection, deepfake regulation, synthetic media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review