Artificial Intelligence into Multimedia Deepfakes Creation and Detection
Abstract
Artificial intelligence has enabled deepfakes, or fake videos that closely resemble real ones, commercially viable (AI). As according to our research, people are less likely to believe social media news, but they are also more likely to be dubious than to be fooled by deepfakes. This finding combines ideas about how effective visual communication is and how uncertainty weakens public discourse confidence. The methodology for this article will based on reviewing the related articles to explore the main components and solutions in the deepfakes. In this article will study and review the deepfakes and it impacts in different multimedia with AI tools. Therefore, that conclude that deepfakes may increase already existing hazards to countries and online civic cultures by increasing general mistrust and doubt in the most of online resources.
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The authors' conclusion posits that the proliferation of deepfakes can amplify risks to nations and digital civic cultures by fostering a pervasive atmosphere of skepticism and uncertainty toward online information sources.
“The authors’ conclusion posits that the proliferation of deepfakes can amplify risks to nations and digital civic cultures by fostering a pervasive atmosphere of skepticism and uncertainty toward online information sources.”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Khder et al. (2023)
Cites
Vaccari, C., & Chadwick, A. (2020) · Social Media + Society · doi:10.1177/2056305120903408 · Cited by 978 · see citing works
Deepfake Detection: Current Challenges and Next Steps
Lyu, S. (2020) · 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew46912.2020.9105991 · Cited by 234 · see citing works
Politics and porn: how news media characterizes problems presented by deepfakes
Gosse, C., & Burkell, J. (2020) · Critical Studies in Media Communication · doi:10.1080/15295036.2020.1832697 · Cited by 148 · see citing works
Defending the state from digital Deceit: The reflexive securitization of deepfake
Taylor, B. C. (2021) · Critical Studies in Media Communication · doi:10.1080/15295036.2020.1833058 · Cited by 25 · see citing works
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023) · Computers · doi:10.3390/computers12100216 · Cited by 117 · see citing works
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2037 · Cited by 71 · see citing works
- Authors
- Khder, M. A., Shorman, S., Aldoseri, D. T., & Saeed, M. M.
- Affiliations
- Applied Science University (BH)
- ORCID
- Shorman, Samer
- Venue
- 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)
- Publisher
- IEEE
- Published
- 2023-03-08
- Pages
- 1-5
- OpenAlex
- W4366373993
- Cited by
- 27
- References
- 4
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 6 · 2023: 2
- FWCI
- 21.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on IT Innovation and Knowledge Discovery (ITIKD) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfakes, deepfake detection, multimedia, misinformation, social media, trust in online information, uncertainty, visual communication, civic culture
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