DEEPFAKE DETECTION ON SOCIAL MEDIA TWEETS

Authors

  • Mrs.M.Pragathi Reddy, Husna sultana, G . Deepika, V. Lavanya

Keywords:

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Abstract

Recent advancements in natural language production provide an additional tool to manipulate public opinion on social media. Furthermore, advancements in language modelling have significantly strengthened the generative capabilities of deep neural models, empowering them with enhanced skills for content generation. Consequently, text-generative models have become increasingly powerful allowing the adversaries to use these remarkable abilities to boost social bots, allowing them to generate realistic deepfake posts and influence the discourse among the general public.

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References

J. P. Verma and S. Agrawal, ‘‘Big data analytics: Challenges and applications for text, audio, video, and social media data,’’ Int. J. Soft Comput., Artif. Intell. Appl., vol. 5, no. 1, pp. 41–51, Feb. 2016.

H. Siddiqui, E. Healy, and A. Olmsted, ‘‘Bot or not,’’ in Proc. 12th Int. Conf. Internet Technol. Secured Trans. (ICITST), Dec. 2017, pp. 462–463.

M. Westerlund, ‘‘The emergence of deepfake technology: A review,’’ Technol. Innov. Manage. Rev., vol. 9, no. 11, pp. 39–52, Jan. 2019.

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Published

2023-12-23

How to Cite

Mrs.M.Pragathi Reddy, Husna sultana, G . Deepika, V. Lavanya. (2023). DEEPFAKE DETECTION ON SOCIAL MEDIA TWEETS. Pegem Journal of Education and Instruction, 13(4), 427–436. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4004

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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