Abstract
Recent advances in deep learning have empowered media synthesis and alteration to achieve levels of realism that were previously unheard of. Artificial intelligence is a potent tool that may be used to modify digital data, such as images, videos, and audio files, through the use of emerging deepfake technologies. Deepfake technology has the potential to significantly affect the reliability of multimedia data through the synthesis of fake media. Significant ramifications arise from this for individuals, organizations, and society at large. With the pace and accessibility of social media, convincing deepfakes can swiftly reach millions of people and adversely influence public opinion. To this end, we propose a multi-modal feature-based classification model that can distinguish between deepfake and real videos efficiently. We have used prefabricated image features as well as a variety of Convolutional Neural Network (CNN) model-generated features, including ResNet50, ResNet101, VGG16, and VGG19. The fake videos are taken up for further investigation to detect their source of origin. We propose a CNN-based classifier for deepfake detection and also explore the efficiency of multiple feature-based classifiers in this respect. This enables us to evaluate the comparative performance of both. The proposed model achieves an accuracy of 99.06% on deepfake classification and 98.75% on source identification when tested on a publicly available FaceForensics++ dataset.
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References
Goodfellow, I.J., et al.: Generative adversarial networks (2014)
Flynn, A., Clough, J., Cooke, T.: Disrupting and preventing deepfake abuse: exploring criminal law responses to AI-facilitated abuse. In: Powell, A., Flynn, A., Sugiura, L. (eds.) The Palgrave Handbook of Gendered Violence and Technology, pp. 583–603. Palgrave Macmillan, Cham (2021). https://doi.org/10.1007/978-3-030-83734-1_29
Temir, E.: Deepfake: new era in the age of disinformation & end of reliable journalism. Selçuk İletişim 13(2), 1009–1024 (2020)
Shin, S.Y., Lee, J.: The effect of deepfake video on news credibility and corrective influence of cost-based knowledge about deepfakes. Digit. Journal. 10(3), 412–432 (2022)
Kwok, A.O.J., Koh, S.G.M.: Deepfake: a social construction of technology perspective. Curr. Issue Tour. 24(13), 1798–1802 (2021)
Lyu, S.: Deepfake detection: current challenges and next steps. In: 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE (2020)
Tariq, S., Abuadbba, A., Moore, K.: Deepfake in the metaverse: security implications for virtual gaming, meetings, and offices. arXiv preprint arXiv:2303.14612 (2023)
Yang, W., et al.: Avoid-DF: audio-visual joint learning for detecting deepfake. IEEE Trans. Inf. Forensics Secur. 18, 2015–2029 (2023)
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1–11 (2019)
Westerlund, M.: The emergence of deepfake technology: a review. Technol. Innov. Manag. Rev. 9(11) (2019)
Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. (TOG) 38(4), 1–12 (2019)
Korshunova, I., Shi, W., Dambre, J., Theis, L.: Fast face-swap using convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3677–3685 (2017)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)
Koonce, B.: Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization. Springer, Cham (2021). https://doi.org/10.1007/978-1-4842-6168-2
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Agarap, A.F.: Deep learning using rectified linear units (ReLU). arXiv preprint arXiv:1803.08375 (2018)
Patle, A., Chouhan, D.S.: SVM kernel functions for classification. In: 2013 International Conference on Advances in Technology and Engineering (ICATE), pp. 1–9. IEEE (2013)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Rokach, L., Maimon, O.: Decision trees. In: Data Mining and Knowledge Discovery Handbook, pp. 165–192 (2005)
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_62
Abramovich, F., Grinshtein, V., Levy, T.: Multiclass classification by sparse multinomial logistic regression. IEEE Trans. Inf. Theory 67(7), 4637–4646 (2021)
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2387–2395 (2016)
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374(2065), 20150202 (2016)
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Das, A.K., Mukhopadhyay, S., Dalui, A., Bhattacharya, R., Naskar, R. (2023). A Multi-stage Multi-modal Classification Model for DeepFakes Combining Deep Learned and Computer Vision Oriented Features. In: Muthukkumarasamy, V., Sudarsan, S.D., Shyamasundar, R.K. (eds) Information Systems Security. ICISS 2023. Lecture Notes in Computer Science, vol 14424. Springer, Cham. https://doi.org/10.1007/978-3-031-49099-6_13
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