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Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes

by Ejike Joseph Aloke, Joshua Abah
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 42
Year of Publication: 2023
Authors: Ejike Joseph Aloke, Joshua Abah
10.5120/ijais2023451952

Ejike Joseph Aloke, Joshua Abah . Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes. International Journal of Applied Information Systems. 12, 42 ( Nov 2023), 1-14. DOI=10.5120/ijais2023451952

@article{ 10.5120/ijais2023451952,
author = { Ejike Joseph Aloke, Joshua Abah },
title = { Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes },
journal = { International Journal of Applied Information Systems },
issue_date = { Nov 2023 },
volume = { 12 },
number = { 42 },
month = { Nov },
year = { 2023 },
issn = { 2249-0868 },
pages = { 1-14 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number42/enhancing-the-fight-against-social-media-misinformation-an-ensemble-deep-learning-framework-for-detecting-deepfakes/ },
doi = { 10.5120/ijais2023451952 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-11-21T19:28:33+05:30
%A Ejike Joseph Aloke
%A Joshua Abah
%T Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 42
%P 1-14
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Deepfakes are synthetic media that replace someone’s action (source person) with another (target person). Images deepfakes, commonly known as “visual deepfakes,” depict a complicated and contentious high-tech avant-garde phenomenon in the sphere of digital trickery and artificial intelligence. These are highly deceitful and computer-based distortions of static images, commonly photographs, where the appearance of a single individual is painstakingly superimposed onto another in a highly sophisticated manner that seems to be real. Image Deepfakes are easy to generate due to easy access to open-source deepfake generation software applications such as FakeApp. Once it is generated, social media becomes its marketplace where it is easily distributed to engage and deceive millions of users. Most research in this area focuses on using a single deep-learning algorithm on a small dataset in the development of the deepfakes detection model. Therefore this research work is focused on building a robust and efficient deepfakes image detection model using a publicly available dataset from Kaggle comprising one hundred and forty thousand (140,000) images. The model was developed using Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Feed Forward Neural Network (FFNN), and Gated Recurrent Unit (GRU). To make the model more robust and efficient, the ensemble technique was employed to ensemble the individual models and an accuracy of 94.91% was achieved.

References
  1. A. Mitra, S. P. Mohanty, P. Corcoran, and E. Kougianos, “A Novel Machine Learning based Method for Deepfake Video Detection in Social Media,” Proc. - 2020 6th IEEE Int. Symp. Smart Electron. Syst. iSES 2020, no. May 2021, pp. 91–96, 2020, doi: 10.1109/iSES50453.2020.00031.
  2. A. Boutadjine, F. Harrag, K. Shaalan, and S. Karboua, “A comprehensive study on multimedia DeepFakes,” in 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), 2023, pp. 1–6.
  3. R. K. Nielsen, A. Cornia, and A. Kalogeropoulos, “Challenges and opportunities for news media and journalism in an increasingly digital, mobile, and social media environment,” 2016, [Online]. Available: https://rm.coe.int/16806c0385
  4. S. Yadav and N. Tiwari, “Privacy preserving data sharing method for social media platforms,” PLoS One, vol. 18, no. 1, p. e0280182, 2023.
  5. C. Ziems, Y. Vigfusson, and F. Morstatter, “Aggressive, repetitive, intentional, visible, and imbalanced: Refining representations for cyberbullying classification,” in Proceedings of the International AAAI Conference on Web and Social Media, 2020, pp. 808–819.
  6. B. Perrigo, “How to Spot an AI Generated Image Like the Balenciaga Pope’,” 2023. https://time.com/6266606/how-to-spot-deepfake-pope/ (accessed Jun. 01, 2023).
  7. N. A. Mhiripiri and T. Chari, Media law, ethics, and policy in the digital age. IGI Global, 2017.
  8. L. Verdoliva, “Media Forensics and DeepFakes : an overview,” pp. 1–24, 2018.
  9. H. Huang, P. S. Yu, and C. Wang, “An Introduction to Image Synthesis with Generative Adversarial Nets,” pp. 1–17.
  10. T. T. Nguyen, Q. V. H. Nguyen, C. M. Nguyen, D. Nguyen, D. T. Nguyen, and S. Nahavandi, “Deep Learning for Deepfakes Creation and Detection: A Survey,” pp. 1–16, 2019, [Online]. Available: http://arxiv.org/abs/1909.11573
  11. B. Paris and J. Donovan, “DEEPFAKES AND CHEAP FAKES,” 2019.
  12. C. Bregler, M. Covell, and M. Slaney, “Video Rewrite : Driving Visual Speech with Audio,” pp. 1–8, 1997.
  13. J. Vincent, “New AI deepfake app creates nude images of women in seconds,” The Verge, vol. 27, 2019.
  14. et al I. Perov, D. Gao, N. Chervoniy, K. Liu, S. Marangonda, C. Umé, M. Dpfks, C. S. Facenheim, L. RP, J. Jiang, “DeepFaceLab is the leading software for creating deepfakes,” 2020. https://github.com/iperov/DeepFaceLab (accessed Sep. 09, 2023).
  15. R. Chesney and D. Citron, “Deepfakes and the new disinformation war: The coming age of post-truth geopolitics,” Foreign Aff., vol. 98, p. 147, 2019.
  16. R. Brooks, Y. Yuan, Y. Liu, H. Chen, and others, “DeepFake and its Enabling Techniques: A Review,” APSIPA Trans. Signal Inf. Process., vol. 11, no. 2, 2022.
  17. A. M. Almars, “Deepfakes detection techniques using deep learning: a survey,” J. Comput. Commun., vol. 9, no. 5, pp. 20–35, 2021.
  18. P. Fraga-Lamas and T. M. Fernandez-Carames, “Fake news, disinformation, and deepfakes: Leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality,” IT Prof., vol. 22, no. 2, pp. 53–59, 2020.
  19. B. Chesney and D. Citron, “Deep fakes: A looming challenge for privacy, democracy, and national security,” Calif. L. Rev., vol. 107, p. 1753, 2019.
  20. D. Wodajo and S. Atnafu, “Deepfake video detection using convolutional vision transformer,” arXiv Prepr. arXiv2102.11126, 2021.
  21. T. Kirchengast, “Deepfakes and image manipulation: criminalisation and control,” Inf. \& Commun. Technol. Law, vol. 29, no. 3, pp. 308–323, 2020.
  22. U. Scherhag, R. Raghavendra, K. B. Raja, M. Gomez-Barrero, C. Rathgeb, and C. Busch, “On the vulnerability of face recognition systems towards morphed face attacks,” in 2017 5th international workshop on biometrics and forensics (IWBF), 2017, pp. 1–6.
  23. Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,” arXiv Prepr. arXiv1811.00656, 2018.
  24. E. Meskys, J. Kalpokiene, P. Jurcys, and A. Liaudanskas, “Regulating deep fakes: legal and ethical considerations,” J. Intellect. Prop. Law \& Pract., vol. 15, no. 1, pp. 24–31, 2020.
  25. C.-C. Hsu, C.-Y. Lee, and Y.-X. Zhuang, “Learning to detect fake face images in the wild,” in 2018 international symposium on computer, consumer and control (IS3C), 2018, pp. 388–391.
  26. J. Kim, S. Han, and S. S. Woo, “Classifying genuine face images from disguised face images,” in 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 6248–6250.
  27. R. Awhad, S. Jayswal, A. More, and J. Kundale, “Fraudulent Face Image Detection,” in ITM Web of Conferences, 2020, p. 3005.
  28. A. Raza, K. Munir, and M. Almutairi, “A Novel Deep Learning Approach for Deepfake Image Detection,” Appl. Sci., vol. 12, no. 19, p. 9820, 2022.
  29. T. T. Nguyen, C. M. Nguyen, D. T. Nguyen, D. T. Nguyen, and S. Nahavandi, “Deep learning for deepfakes creation and detection,” arXiv Prepr. arXiv1909.11573, vol. 1, no. 2, p. 2, 2019.
  30. xhlulu, “140k Real and Fake Faces.” 2020. [Online]. Available: https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces
  31. B. Rasti et al., “Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox,” IEEE Geosci. Remote Sens. Mag., vol. 8, no. 4, pp. 60–88, 2020.
  32. R. Rafique, R. Gantassi, R. Amin, J. Frnda, A. Mustapha, and A. H. Alshehri, “Deep fake detection and classification using error-level analysis and deep learning,” Sci. Rep., vol. 13, no. 1, p. 7422, 2023.
  33. A. Ismail, M. Elpeltagy, M. S. Zaki, and K. Eldahshan, “A new deep learning-based methodology for video deepfake detection using XGBoost,” Sensors, vol. 21, no. 16, p. 5413, 2021.
  34. J. Bernal et al., “Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,” Artif. Intell. Med., vol. 95, pp. 64–81, 2019, doi: https://doi.org/10.1016/j.artmed.2018.08.008.
  35. Y. Patel et al., “An Improved Dense CNN Architecture for Deepfake Image Detection,” IEEE Access, vol. 11, pp. 22081–22095, 2023.
  36. H. S. Shad et al., “Comparative analysis of deepfake image detection method using convolutional neural network,” Comput. Intell. Neurosci., vol. 2021, 2021.
  37. A. Karandikar, V. Deshpande, S. Singh, S. Nagbhidkar, and S. Agrawal, “Deepfake video detection using convolutional neural network,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, pp. 1311–1315, 2020.
  38. K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” J. Big data, vol. 3, no. 1, pp. 1–40, 2016.
  39. C. Desai, “Image classification using transfer learning and deep learning,” Int. J. Eng. Comput. Sci., vol. 10, no. 9, pp. 25394–25398, 2021.
  40. H. Agarwal, A. Singh, and D. Rajeswari, “Deepfake Detection Using SVM,” in 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 2021, pp. 1245–1249.
  41. 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 Comput. Sci., vol. 2, no. 2, pp. 1–18, 2021, doi: 10.1007/s42979-021-00495-x.
  42. L. A. Passos, D. Jodas, K. A. P. da Costa, L. A. S. Júnior, D. Colombo, and J. P. Papa, “A review of deep learning-based approaches for deepfake content detection,” arXiv Prepr. arXiv2202.06095, 2022.
  43. J. L. Gayathri, B. Abraham, M. S. Sujarani, and M. S. Nair, “A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network,” Comput. Biol. Med., vol. 141, p. 105134, 2022.
  44. P. Le-Hong and A.-C. Le, “A comparative study of neural network models for sentence classification,” in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 360–365.
  45. Z. Chang, X. Zhang, S. Wang, S. Ma, and W. Gao, “Iprnn: An Information-Preserving Model For Video Prediction Using Spatiotemporal Grus,” 2021, pp. 2703–2707. doi: 10.1109/ICIP42928.2021.9506391.
  46. C. B. C. Latha and S. C. Jeeva, “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques,” Informatics Med. Unlocked, vol. 16, p. 100203, 2019.
  47. N. Altman and M. Krzywinski, “Ensemble methods: bagging and random forests,” Nat. Methods, vol. 14, no. 10, pp. 933–935, 2017.
  48. H. Drucker, C. Cortes, L. D. Jackel, Y. LeCun, and V. Vapnik, “Boosting and other ensemble methods,” Neural Comput., vol. 6, no. 6, pp. 1289–1301, 1994.
  49. M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. \& Manag., vol. 45, no. 4, pp. 427–437, 2009.
  50. D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv Prepr. arXiv2010.16061, 2020.
  51. S. H. Kok, A. Azween, and N. Z. Jhanjhi, “Evaluation metric for crypto-ransomware detection using machine learning,” J. Inf. Secur. Appl., vol. 55, p. 102646, 2020.
  52. I. Idrissi, M. Azizi, and O. Moussaoui, “Accelerating the update of a DL-based IDS for IoT using deep transfer learning,” Indones. J. Electr. Eng. Comput. Sci, vol. 23, no. 2, pp. 1059–1067, 2021.
  53. F. Muharemi, D. Logof\uatu, and F. Leon, “Machine learning approaches for anomaly detection of water quality on a real-world data set,” J. Inf. Telecommun., vol. 3, no. 3, pp. 294–307, 2019.
  54. M. Hossin and M. N. Sulaiman, “A review on evaluation metrics for data classification evaluations,” Int. J. data Min. \& Knowl. Manag. Process, vol. 5, no. 2, p. 1, 2015.
Index Terms

Computer Science
Information Sciences
Image Recognition
Security

Keywords

Deepfakes Misinformation Social media Ensemble Method

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