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Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

Shoaib, Mohamed R., Wang, Zefan, Ahvanooey, Milad Taleby, Zhao, Jun·2023·2023 International Conference on Computer and Applications (ICCA)
DOI 10.1109/icca59364.2023.10401723
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  • Concerns persist regarding the reliability of GenAI outputs, as large AI models can produce content that appears highly convincing yet may be false or misleading.

    “Simultaneously, concerns persist regarding the reliability of GenAI outputs, as large AI models can produce content that appears highly convincing yet may be false or misleading.”

    From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [24]

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Research works printed but not linked: 33

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  3. V. Danry, J. Leong, P. Pataranutaporn, P. Tandon, Y. Liu, R. Shilkrot, P. Punpongsanon, T. Weissman, P. Maes, and M. Sra, "AI-generated characters: putting deepfakes to good use," in CHI Conference on Human Factors in Computing Systems Extended Abstracts, 2022, pp. 1-5.
  4. N. Amezaga and J. Hajek, "Availability of voice deepfake technology and its impact for good and evil," in Proceedings of the 23rd Annual Conference on Information Technology Education, 2022, pp. 23-28.
  5. J. Fletcher, "Deepfakes, artificial intelligence, and some kind of dystopia: The new faces of online post-fact performance," Theatre Journal, vol. 70, no. 4, pp. 455-471, 2018.
  6. R. W. Zmud, "Opportunities for strategic information manipulation through new information technology," Organizations and Communication Technology, pp. 95-116, 1990.
  7. D. Silverman, K. Kaltenthaler, and M. Dagher, "Seeing is disbelieving: the depths and limits of factual misinformation in war," International Studies Quarterly, vol. 65, no. 3, pp. 798-810, 2021.
  8. M. T. Ahvanooey, Q. Li, X. Zhu, M. Alazab, and J. Zhang, "ANiTW: A novel intelligent text watermarking technique for forensic identification of spurious information on social media," Computers & Security, vol. 90, p. 101702, 2020.
  9. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in Neural Information Processing Systems, vol. 27, 2014.
  10. B. He, Y. Hu, Y. Lee, S. Oh, G. Verma, and S. Kumar, "A survey on the role of crowds in combating online misinformation: Annotators, evaluators, and creators," arXiv, 2023, accessed November 21, 2023.
  11. S. Siwakoti, J. N. Shapiro, and N. Evans, "Less reliable media drive inter- est in anti-vaccine information," Harvard Kennedy School Misinformation Review, 2023.
  12. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., "Language models are few-shot learners," Advances in Neural Information Processing Systems, vol. 33, pp. 1877-1901, 2020.
  13. S. Biderman, H. Schoelkopf, Q. G. Anthony, H. Bradley, K. O'Brien, E. Hallahan, M. A. Khan, S. Purohit, U. S. Prashanth, E. Raff et al., "Pythia: A suite for analyzing large language models across training and scaling," in International Conference on Machine Learning. PMLR, 2023, pp. 2397-2430.
  14. D. Xu, S. Fan, and M. Kankanhalli, "Combating misinformation in the era of generative AI models," in Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 9291-9298.
  15. Y. Mirsky, A. Demontis, J. Kotak, R. Shankar, D. Gelei, L. Yang, X. Zhang, M. Pintor, W. Lee, Y. Elovici et al., "The threat of offensive ai to organizations," Computers & Security, vol. 124, p. 103006, 2023.
  16. U. A. Ciftci, G. Yuksek, and I. Demir, "My face my choice: Privacy enhancing deepfakes for social media anonymization," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 1369-1379.
  17. K. Wahl-Jorgensen and M. Carlson, "Conjecturing fearful futures: jour- nalistic discourses on deepfakes," Journalism Practice, vol. 15, no. 6, pp. 803-820, 2021.
  18. C. Campbell, K. Plangger, S. Sands, and J. Kietzmann, "Preparing for an era of deepfakes and ai-generated ads: A framework for understanding responses to manipulated advertising," Journal of Advertising, vol. 51, no. 1, pp. 22-38, 2022.
  19. A. Mitra, S. P. Mohanty, P. Corcoran, and E. Kougianos, "A machine learning based approach for deepfake detection in social media through key video frame extraction," SN Computer Science, vol. 2, pp. 1-18, 2021.
  20. H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, and N. Yu, "Multi- attentional deepfake detection," in Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, 2021, pp. 2185-2194.
  21. M. Masood, M. Nawaz, K. M. Malik, A. Javed, A. Irtaza, and H. Malik, "Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward," Applied Intelligence, vol. 53, no. 4, pp. 3974-4026, 2023.
  22. D. Krishna, "Deepfakes, online platforms, and a novel proposal for transparency, collaboration, and education," Rich. JL & Tech., vol. 27, p. 1, 2020.
  23. J. T. Hancock and J. N. Bailenson, "The social impact of deepfakes," pp. 149-152, 2021.
  24. P. Gupta, K. Chugh, A. Dhall, and R. Subramanian, "The eyes know it: Fakeet-an eye-tracking database to understand deepfake perception," in Proceedings of the 2020 International Conference on Multimodal Interaction, 2020, pp. 519-527.
  25. K. Kikerpill, A. Siibak, and S. Valli, "Dealing with deepfakes: Reddit, on- line content moderation, and situational crime prevention," in Theorizing Criminality and Policing in the Digital Media Age. Emerald Publishing Limited, 2021, vol. 20, pp. 25-45.
  26. B. Giovanola and S. Tiribelli, "Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algo- rithms," AI & Society, vol. 38, no. 2, pp. 549-563, 2023.
  27. A. Henriksen, S. Enni, and A. Bechmann, "Situated accountability: Ethical principles, certification standards, and explanation methods in applied AI," in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 574-585.
  28. H.-C. Yang, A. R. Rahmanti, C.-W. Huang, and Y.-C. J. Li, "How can research on artificial empathy be enhanced by applying deepfakes?" Journal of Medical Internet Research, vol. 24, no. 3, p. e29506, 2022.
  29. D. Kelly and J. Burkell, "It's not (all) about the information: The role of cognition in creating and sustaining false beliefs," Cambridge Studies on Governing Knowledge Commons, 2024.
  30. P. T. Jaeger and N. G. Taylor, "Arsenals of lifelong information literacy: Educating users to navigate political and current events information in world of ever-evolving misinformation," The Library Quarterly, vol. 91, no. 1, pp. 19-31, 2021.
  31. Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, and A. Jamalipour, "Blockchain-empowered lifecycle management for AI-generated content (AIGC) products in edge networks," arXiv preprint arXiv:2303.02836, 2023. · arXiv
  32. K. Langmia, Black Communication in the Age of Disinformation: Deep- Fakes and Synthetic Media. Springer Nature, 2023.
  33. N. Kshetri, "The economics of deepfakes," Computer, vol. 56, no. 8, pp. 89-94, 2023.

Cited by

Sorted by citations

Authors
Shoaib, M. R., Wang, Z., Ahvanooey, M. T., & Zhao, J.
Affiliations
Nanyang Technological University,School of Computer Science and Engineering,Singapore
Venue
2023 International Conference on Computer and Applications (ICCA)
Publisher
IEEE
Published
2023-11-28
Pages
1-7
DOI
10.1109/icca59364.2023.10401723
Cited by
124
References
13
Licence
Funders
  • Ministry of Education
  • Nanyang Technological University

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