Authors : Harish Chaudhary; Nandeesh C. R; Gagan T. N; V. Tejas Aarya; Dr. Shakunthala B. S; Chethan Kumar T.

Volume/Issue : Volume 10 - 2025, Issue 12 - December

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Google Scholar : https://tinyurl.com/5n72kwfy

Scribd : https://tinyurl.com/3f3kcvfh

DOI : https://doi.org/10.38124/ijisrt/25dec111

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Abstract : The study presents a three-stage framework leveraging advanced deep learning techniques to enhance deepfake detection across multimedia datasets—image, audio, and video. The initial stage utilizes an Xception Net-based model achieving 95.56% accuracy for image detection via depth-wise separable convolutions on the CelebA dataset. The second stage employs a hybrid CNN and LSTM approach for audio analysis, achieving 98.5% accuracy on the DEEP-VOICE dataset. The final stage integrates XceptionNet and LSTM for video detection, yielding 97.574% accuracy across multiple datasets. To improve model robustness, class weighting addresses dataset imbalances. This research advances detection methodologies, crucial for maintaining digital integrity and combating misinformation.

Keywords : Deepfake, Convolutional Neural Networks, Long Short-Term Memory, XceptionNet, Celeb Dataset.

References :

1. Agarwal, S., Farid, H., El-Gaaly, T. and Lim, S. N.: Detecting deep-fake videos from appearance and behavior, In: Proc. Of IEEE international workshop on information forensics and security (WIFS), pp. 1-6 (2020).
2. Müller, N. M., Czempin, P., Dieckmann, F., Froghyar, A., and Böttinger, K.: Does audio deepfake detection generalize? arXiv preprint, arXiv:2203.16263 (2022).
3. Lyu, S., Deepfake detection: Current challenges and next steps. In: Proc. Of 2020 IEEE international conference on multimedia & expo workshops (ICMEW), London, UK, pp. 1-6, (2020).
4. Goodfellow I., Jean, PA., Mehdi, M., Bing, X., David, WF., Sherjil, O., Aaron, C. and Yoshua, B.: Generative adversarial networks., Commun. ACM, 63(11), pp. 139-144 (2020).
5. Eiter, T. and Mannila, H.: Computing discrete Fréchet distance, technical report CD- TR 94/64, Technische Universitat Wien (1994).
6. Došilović, F.K., Brčić, M., and Hlupić, N.: Explainable artificial intelligence: A survey. In: Proc. Of 41st International convention on information and communication technology, electronics, and microelectronics (MIPRO), Opatija, Croatia, pp. 0210- 0215 (2018).
7. Guo, W.: Explainable artificial intelligence for 6G: Improving trust between human and machine, IEEE Communications Magazine, 58(6), pp. 39-45 (2020).
8. Salih, A., Raisi-Estabragh, Z., Galazzo, I. B., Radeva, P., Petersen, S.E., Menegaz G. and Lekadir, K.: Commentary on explainable artificial intelligence methods: SHAP and LIME, arXiv preprint arXiv:2305.02012 (2023).
9. Lujain I., Mesinovic, M., Yang, K. W., and Eid, M. A.: Explainable prediction of acute myocardial infarction using machine learning and shapley values, IEEE Access, (8), pp. 210410-210417, 2020.
10. Ramprasaath, S. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. Grad cam: Visual explanations from deep networks via gradient-based localization. In: Proc. of IEEE international conference on computer vision (ICCV), Venice, Italy, pp. 618-626 (2017).