A Survey on Multimodal Deepfake Detection System

DOI

10.2991/978-94-6239-616-6_88How to use a DOI?

Keywords

Deepfake detection; multimodal learning; artificial intelligence; generative adversarial network (GANs); video forensics; audio-visual analysis; cross-modal synchronization; lip-sync detection; facial artifacts

Abstract

Deepfake technology has advanced rapidly, leveraging deep learning to manipulate image, audio, and video content with increasing realism. These developments pose significant threats to digital security, privacy, and trust. Traditional detection methods, which typically focus on unimodal analysis (analysing only video or audio), are becoming insufficient against sophisticated multimodal deepfakes. This survey paper reviews the current state of multimodal deepfake detection. We analyse existing literature to highlight the shift from unimodal to multimodal detection strategies, examining how the fusion of audio, video, and image analysis enhances detection accuracy. Furthermore, this survey identifies key challenges such as cross-modal synchronization, dataset scarcity, and generalization across domains. Finally, we discuss ethical considerations, including fairness and explainability, and outline future directions for robust forensic analysis.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - D. Prabhu
AU  - S. Kishore Kanna
AU  - R. Hemachandiran
AU  - Madugula Jagadeesh
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on Multimodal Deepfake Detection System
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1202
EP  - 1210
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_88
DO  - 10.2991/978-94-6239-616-6_88
ID  - Prabhu2026
ER  -