Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications

Samer Hussain Al-khazrajı; Hassan Hadi Saleh; Adil Ibrahim Khalıd; Israa Adnan Mıshkhal

doi:10.55549/epstem.1371792

Conference Paper

Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications

Year 2023, Volume: 23, 429 - 441, 30.09.2023

Samer Hussain Al-khazrajı Hassan Hadi Saleh Adil Ibrahim Khalıd Israa Adnan Mıshkhal

https://doi.org/10.55549/epstem.1371792

Abstract

Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes crucial. This research investigates the detection, misinformation, and societal implications of deepfake technology on social media. Through a comprehensive literature review, the study examines the development and capabilities of deepfakes, existing detection techniques, and challenges in identifying them. The role of deepfakes in spreading misinformation and disinformation is explored, highlighting their potential consequences on public trust and social cohesion. The societal implications and ethical considerations surrounding deepfakes are examined, along with legal and policy responses. Mitigation strategies, including technological advancements and platform policies, are discussed. By shedding light on these critical aspects, this research aims to contribute to a better understanding of the impact of deepfake technology on social media and to inform future efforts in detection, prevention, and policy development.

Keywords

Deepfake , Social media , Artificial intelligence , Generative adversarial networks , Deep neural networks.

References

Ali, S., DiPaola, D., & Breazeal, C. (2021). What are GANs?: Introducing generative adversarial networks to middle school students. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15472-15479).
Bateman, J. (2020). Carnegie endowment for international peace. https://carnegieendowment.org/2020/07/08/deepfakes-and-synthetic-media-in-financial-system-assessing-threat-scenarios-pub-82237
Borji, A. (2023). Qualitative failures of image generation models and their application in detecting deepfakes. arXiv, 1.

Year 2023, Volume: 23, 429 - 441, 30.09.2023

Samer Hussain Al-khazrajı Hassan Hadi Saleh Adil Ibrahim Khalıd Israa Adnan Mıshkhal

https://doi.org/10.55549/epstem.1371792

Abstract

References

Ali, S., DiPaola, D., & Breazeal, C. (2021). What are GANs?: Introducing generative adversarial networks to middle school students. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15472-15479).
Bateman, J. (2020). Carnegie endowment for international peace. https://carnegieendowment.org/2020/07/08/deepfakes-and-synthetic-media-in-financial-system-assessing-threat-scenarios-pub-82237
Borji, A. (2023). Qualitative failures of image generation models and their application in detecting deepfakes. arXiv, 1.

There are 3 citations in total.

Details

Primary Language	English
Subjects	Software Engineering (Other)
Journal Section	Articles
Authors	Samer Hussain Al-khazrajı Hassan Hadi Saleh Adil Ibrahim Khalıd Israa Adnan Mıshkhal
Early Pub Date	October 5, 2023
Publication Date	September 30, 2023
Published in Issue	Year 2023 Volume: 23

Cite

APA	Al-khazrajı, S. H., Saleh, H. H., Khalıd, A. I., Mıshkhal, I. A. (2023). Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 429-441. https://doi.org/10.55549/epstem.1371792