Next-Generation Deepfake Detection: A Multimodal Framework for Media Authenticity and Misinformation Neutralization

Author

Arun S John, Prof. Riya Prakash, Prof (Dr). L.C. Manikandan

Abstract

 The explosion of highly convincing artificial media, fueled by sophisticated generative artificial intelligence, has triggered a severe breakdown in digital reliability and information security. Existing single-modality detection systems struggle to keep pace with modern diffusion networks and Generative Adversarial Networks (GANs), failing to detect zero-day forgeries due to localized artifact repair and cross-domain generalization issues. To resolve these limitations, this paper presents a modern technological infrastructure for identifying deepfakes and analyzing misinformation that moves beyond the constraints of older, single-mode detection strategies. By utilizing an innovative multimodal fusion design, this framework combines Vision Transformers (ViT) to isolate spatial anomalies, self-supervised learning for assessing temporal uniformity, and Large Language Models (LLMs) to authenticate narrative semantics. Rather than depending on easily circumvented visual errors like earlier systems, this advanced strategy measures the overarching consistency of digital media by aligning disparate data channels, including audio waveforms, lip synchronization, and metadata context. Additionally, incorporating distributed ledger technology establishes an unalterable record of digital origin, delivering a decentralized method for confirming media authenticity. This system achieves exceptional precision in detecting novel, zero-day deepfakes produced by advanced diffusion networks and Generative Adversarial Networks (GANs). Furthermore, it utilizes natural language processing to identify and neutralize the spread of synthetic narratives across online platforms. Ultimately, this framework provides a highly adaptable, scalable, and robust shield against the malicious use of AI in today's digital environment.

Keywords

Deepfake Detection, Multimodal Fusion, Vision Transformers (ViT), Large Language Models (LLMs), Cross-Attention Mechanisms, Digital Provenance, Misinformation Analysis, Latent Diffusion Models

Full Text:

Download Paper PDF

References

[1] A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," in International Conference on Learning Representations (ICLR), 2021.

[2] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, "High-resolution image synthesis with latent diffusion models," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10684-10695.

[3] A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, "ViViT: A video vision transformer," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6836-6846.

[4] A. Masood et al., "A survey on the detection and impacts of deepfakes in visual, audio, and textual formats," IEEE Access, vol. 11, pp. 144497-144529, 2023.

[5] Y. Miao et al., "Multimodal Deepfake Detection Using Transformer-Based Large Language Models," The American Journal of Engineering and Technology, vol. 7, no. 5, pp. 169-177, 2025.

[6] I. Goodfellow et al., "Generative adversarial nets," in Advances in Neural Information Processing Systems (NeurIPS), vol. 27, 2014, pp. 2672-2680.

[7] A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, "FaceForensics++: Learning to detect manipulated facial images," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1-11.

[8] T. Karras, S. Laine, and T. Aila, "A style-based generator architecture for generative adversarial networks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4401-4410.

[9] A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.

[10] Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, "Celeb-DF: A large-scale challenging dataset for deepfake forensics," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3207-3216.

[11] A. Haliassos, K. Vougioukas, S. Petridis, and M. Pantic, "Lips don't lie: A generalisable and robust approach to face forgery detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5039-5049.

[12] R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, "Deepfakes and beyond: A survey of face manipulation and fake detection," Information Fusion, vol. 64, pp. 131-148, 2020.

[13] S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, and H. Li, "Protecting world leaders against deep fakes," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 38-45.

[14] D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, "MesoNet: a compact facial video forgery detection network," in IEEE International Workshop on Information Forensics and Security (WIFS), 2018, pp. 1-7.

[15] R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner, and Y. Choi, "Defending against neural fake news," in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.

[16] H. R. Hasan and K. Salah, "Combating deepfake videos using blockchain and smart contracts," IEEE Access, vol. 7, pp. 41596-41606, 2019.

Share your valuable work from Social Media Buttons