Abstract
Social media platforms play a major role in dissemination of information across the world. The users of social media platforms like Twitter, Facebook, whatsapp etc. are exposed to enormous number of posts on various topics. It becomes infeasible for the users to decide the credibility of each message. As per Datareportal April 2023 global overview, there are 4.8 billion social media users equating to 59.9% of the world’s population. As per PEW Research Center, 2021 report “About half of the Americans get news from Social Media” as opposed to TV, news platforms, or the newspaper. The Fake content posted on Social Networks purporting to be authentic information has misleading effect on the readers. Therefore, it is very vital to detect false information posted on social media before it proliferates around the world. This paper addresses three types of fake content: Text, Image and Audio. This work presents a framework which is a fusion of three models to identify fake text, audio and images. Best techniques like Error Level Analysis, CNN for identifying fake images; Gensim, NLP for detecting phony Text content and Librosa, MFCC for detecting fraudulent audio content are identified and used. The proposed work achieved an accuracy of 94.9% for text, 87.0% for image and 99.8% for audio with minimum number of epochs.
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References
Study: On Twitter, false news travels faster than true stories. https://news.mit.edu/2018/study-twitter-false-news-travels-faster-true-stories-0308. Accessed 04 May 2023
Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L.: Detection and visualization of misleading content on Twitter (2017). Springer London Ltd, Part of Springer Nature
Viral Images From 2021 That Were Totally Fake. https://gizmodo.com/9-viral-images-from-2021-that-were-totally-fake-1848250856/slides/8. Accessed 04 May 2023
de Rezende, E.R.S., Ruppert, G.C.S., Carvalho, T.: Detecting computer generated images with deep CNN. In: SIBGRAPI Conference (2017)
Villan, M.A., Kuruvilla, K., Paul, J., Elias, E.P.: Fake image detection using machine learning. IRACST – Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) 7(2) (2017)
Singh, B., Sharma, D.K.: Predicting image credibility in fake news over social media using multi-modal approach. Neural Comput. Appl. 34, 21503–21517 (2021). Springer Nature
Kaur, S., Kumar, P., Kumaraguru, P., Automating fake news detection system using multi-level voting model. Soft Comput. 24(12), 9049–9069 (2019)
Korshunov, P., Marcel, S.: Speaker inconsistency detection in tampered video. In: 26th European Signal Processing Conference (EUSIPCO) (2018)
Nasar, B.F., Sajini, T., Lason, E.R.: Deepfake detection in media files - audios, images and videos. In: IEEE Recent Advances in Intelligent Computational Systems (RAICS), 03–05 December 2020
Liu, T., Yan, D., Wang, R., Yan, N., Chen, G.: Identification of fake stereo audio using SVM and CNN. Information 12(7), 263 (2021)
Li, Y., Chang, M.-C., Lyu, S., Oculi, I.I.: Exposing AI generated fake face videos by detecting eye blinking. In: IEEE International Workshop on Information Forensics and Security (WIFS). IEEE (2018)
Lunagaria, S., Parekh, C., Fake audio speech detection. IJIRT 7(1) (2020). ISSN 2349-6002
Sudiatmika, I.B.K., Rahman, F., Trisno, T., Suyoto, S.: Image forgery detection using error level analysis and deep learning. TELKOMNIKA (Telecommun. Comput. Electron. Control) 17(2), 653–659 (2018)
Majumder, Md.T.H., Alim Al Islam, A.B.M.: A tale of a deep learning approach to image forgery detection. In: 5th International Conference on Networking, Systems and Security (NSysS), pp. 1–9. IEEE (2018)
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Pavani, N., Shyamala, K. (2024). Scrutinization of Text, Images and Audio Posts on Social Media for Identifying Fake Content. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_22
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DOI: https://doi.org/10.1007/978-3-031-48879-5_22
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