Development Of Artificial Intelligence-Based Model for Forensic Analysis of Cross-Platform Deepfakes

Synthetic media, a product of advancements in artificial intelligence (AI) and machine learning, represents a transformative innovation that reshapes how content is created, manipulated and consumed. One of the most advanced technology in synthetic media is deepfakes. The misuse of deepfakes poses significant threats to privacy, security and societal trust. Cybercriminals exploit this technology for phishing scams, identity theft and spreading misinformation. This study developed an AI-based hybrid model for forensic analysis of cross platform deep fakes to address these gaps. The study developed a hybrid model that integrates 2D CNN, 1D CNN and RNN framework capable of isolating platform-specific spatial and temporal features for forensic analysis of cross-platform deepfakes. The platforms include social media such as Facebook, Instagram, Youtube, Tiktok and Twiter. The research used Celeb DF dataset for training, validation and testing of the hybrid model. Result from the evaluation metrics showed that the model achieved an accuracy of 99 % on the training data and 93.5 % on the test data. Result equally showed that precision, recall and MCC value were 96.8 %, 90 % and 87.2 % respectively on the test data. The model outperformed single CNN and RNN models and some hybrid models reported. This study demonstrated a reliable hybrid model with high detection accuracy for the forensic analysis of deepfakes. Hence, the model has the potential to address the problem of misinformation caused by synthetic media.

Artificial Intelligence, forensic analysis, 2D CNN, RNN, Deepfakes, Hybrid model

1. Agarwal, H., & Singh, A. (2021). Deepfake detection using svm. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) 1245-1249. [Google Scholar] [Crossref]

2. Reis, P. M. G. I., & Ribeiro, R. O. (2024). A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores. Forensic Science International, 358, 111747. [Google Scholar] [Crossref]

3. Chen, H. S., Rouhsedaghat, M., Ghani, H., Hu, S., You, S., & Kuo, C. C. J. (2021). Defakehop: A lightweight high-performance deepfake detector. In 2021 IEEE International conference on Multimedia and Expo (ICME) 1-6. [Google Scholar] [Crossref]

4. Sunil, R., Mer, P., Diwan, A., Mahadeva, R., & Sharma, A. (2025). Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation. Heliyon e42273. [Google Scholar] [Crossref]

5. Choi, Y., Uh, Y., Yoo, J., & Ha, J. W. (2020). Stargan v2: Diverse image synthesis for multiple domains. [Google Scholar] [Crossref]

6. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 81888197). [Google Scholar] [Crossref]

7. Chen, H., & Magramo, K. (2024). Finance worker pays out $25 million after video call with deepfake ‘chief financial officer’(2024). URL https://www. cnn. com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intlhnk/index. html. [Google Scholar] [Crossref]

8. Arshad, H., Jantan, A. B., & Abiodun, O. I. (2018). Digital forensics: Review of issues in scientific validation of digital evidence. Journal of Information Processing Systems, 14(2). [Google Scholar] [Crossref]

9. Sharma, B., Ghawaly, J., McCleary, K., Webb, A. M., & Baggili, I. (2025). ForensicLLM: A local large language model for digital forensics. Forensic Science International: Digital Investigation, 52, 301872. [Google Scholar] [Crossref]

10. Lukas, N., Salem, A., Sim, R., Tople, S., Wutschitz, L., & Zanella-Béguelin, S. (2023). Analysing leakage of personally identifiable information in language models. In 2023 IEEE Symposium on Security and Privacy (SP) 346-363. [Google Scholar] [Crossref]

11. Horsman, G., & Dodd, A. (2024). Competence in digital forensics. Forensic Science International: Digital Investigation, 51, 301840. [Google Scholar] [Crossref]

12. de Rancourt-Raymond, A., & Smaili, N. (2023). The unethical use of deepfakes. Journal of Financial Crime, 30(4), 1066-1077. [Google Scholar] [Crossref]

13. Chicco, D., & Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Mining, 16(1), 4. [Google Scholar] [Crossref]

14. Viola, M., & Voto, C. (2023). Designed to abuse? Deepfakes and the non-consensual diffusion of intimate images. Synthese, 201(1), 30. [Google Scholar] [Crossref]

15. Batagelj, B., Kronovšek, A., Štruc, V., & Peer, P. (2025). Robust cross-dataset deepfake detection with multitask self-supervised learning. ICT Express. [Google Scholar] [Crossref]

16. Kosarkar, U., Sarkarkar, G., & Gedam, S. (2023). Revealing and classification of deepfakes video's images using a customize convolution neural network model. Procedia Computer Science, 218, 2636-2652. [Google Scholar] [Crossref]

17. Almestekawy, A., Zayed, H. H., & Taha, A. (2024). Deepfake detection: Enhancing performance with spatiotemporal texture and deep learning feature fusion. Egyptian Informatics Journal, 27, 100535. [Google Scholar] [Crossref]

18. Jafar, M. T., Ababneh, M., Al-Zoube, M., & Elhassan, A. (2020). Forensics and analysis of deepfake videos. In 2020 11th international conference on information and communication systems (ICICS) 053058. [Google Scholar] [Crossref]

19. Koritala, S. P., Chimata, M., Polavarapu, S. N., Vangapandu, B. S., Gogineni, T. K., & Manikandan, V. M. (2024, June). A Deepfake detection technique using Recurrent Neural Network and EfficientNet. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE. [Google Scholar] [Crossref]

20. Suratkar, S., & Kazi, F. (2023). Deep fake video detection using transfer learning approach. Arabian Journal for Science and Engineering, 48(8), 9727-9737. [Google Scholar] [Crossref]

21. Petmezas, G., Vanian, V., Konstantoudakis, K., Almaloglou, E. E., & Zarpalas, D. (2025). Video deepfake detection using a hybrid CNN-LSTM-Transformer model for identity verification. Multimedia Tools and Applications, 1-20. [Google Scholar] [Crossref]

22. Sharma, P., Kumar, M., & Sharma, H. K. (2024). GAN-CNN ensemble: a robust deepfake detection model of social media images using minimised catastrophic forgetting and generative replay technique. Procedia Computer Science, 235, 948-960. [Google Scholar] [Crossref]

23. Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020). Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3207-3216). [Google Scholar] [Crossref]

24. Vamsi, V. V. V. N. S., Shet, S. S., Reddy, S. S. M., Rose, S. S., Shetty, S. R., Sathvika, S., ... & Shankar, S. P. (2022). Deepfake detection in digital media forensics. Global Transitions Proceedings, 3(1), 74-79. [Google Scholar] [Crossref]

25. Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020). Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5203-5212). [Google Scholar] [Crossref]

26. Dong, F., Zou, X., Wang, J., & Liu, X. (2023). Contrastive learning-based general Deepfake detection with multi-scale RGB frequency clues. Journal of King Saud UniversityComputer and Information Sciences, 35(4), 90-99. [Google Scholar] [Crossref]

27. Sohail, S., Sajjad, S. M., Zafar, A., Iqbal, Z., Muhammad, Z., & Kazim, M. (2025). Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning. Information, 16(4), 270. [Google Scholar] [Crossref]

28. Cuellar, M. (2024). The Neglected Error: False Negatives and the Case for Validating Eliminations. arXiv preprint arXiv:2412.05398. [Google Scholar] [Crossref]

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