By: Brij B. Gupta, Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan. Email: bbgupta@asia.edu.tw
Abstract
In an era where digital media is omnipresent, deep fakes have emerged as a potent tool for both creativity and deception. Leveraging advanced artificial intelligence, deep fakes manipulate audio and visual content to create hyper-realistic fabrications that are often indistinguishable from genuine media. This blog explores the intricate relationship between deep fakes and social media, highlighting the technology’s evolution, its widespread impact, and the challenges it poses. By examining the psychological, ethical, and legal ramifications, as well as the current and future strategies for detection and prevention, this blog aims to provide a comprehensive understanding of how deep fakes are reshaping our digital reality and what steps can be taken to navigate this new landscape responsibly.
Introduction
Deep fakes are videos created using artificial intelligence to depict individuals saying or doing things that never occurred [1]. They pose challenges as they blur the lines between reality and fiction, making it difficult to discern authentic content from manipulated ones [2]. The ability of deep fakes to deceive viewers raises concerns about their potential misuse in spreading disinformation, image-based sexual abuse, and undermining trust in media [3][4]. Detecting deep fakes is crucial, with advancements in machine learning and deep learning techniques being pivotal in identifying and combatting this emerging threat [5][6]. Understanding deep fakes is essential in navigating the evolving landscape of misinformation and safeguarding against the manipulation of digital content [7].
Read the full article at Medium
References
- I. Sharma, K. Jain, A. Behl, A. Baabdullah, M. Giannakis, & Y. Dwivedi, “Examining the motivations of sharing political deepfake videos: the role of political brand hate and moral consciousness”, Internet Research, vol. 33, no. 5, p. 1727-1749, 2023. https://doi.org/10.1108/intr-07-2022-0563
- M. Muqsith and R. Pratomo, “The development of fake news in the post-truth age”, Salam Jurnal Sosial Dan Budaya Syar I, vol. 8, no. 5, p. 1391-1406, 2021. https://doi.org/10.15408/sjsbs.v8i5.22395
- A. Fernandez, ““deep fakes”: disentangling terms in the proposed eu artificial intelligence act”, Ufita – Archiv Für Medienrecht Und Medienwissenschaft, vol. 85, no. 2, p. 392-433, 2021. https://doi.org/10.5771/2568-9185-2021-2-392
- X. Fang, “Forged facial video detection framework based on multi-region temporal relationship feature”, Aip Advances, vol. 13, no. 8, 2023. https://doi.org/10.1063/5.0125032
- R. Varma, Y. Verma, P. Vijayvargiya, & P. Churi, “A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-covid-19 pandemic”, International Journal of Intelligent Computing and Cybernetics, vol. 14, no. 4, p. 617-646, 2021. https://doi.org/10.1108/ijicc-04-2021-0069
Cite As
Gupta BB (2024) Deep Fakes and Social Media Navigating the New Reality, Insights2Techinfo, pp.1