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A Survey on Deepfake Video Detection using Hybrid Multimodal Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 82 |
| Year of Publication: 2026 |
| Authors: Adithya Anil, Afeefa M.S., Akshara Raghu, Anamika Sudheer, Shimy Joseph |
10.5120/ijca2026926440
|
Adithya Anil, Afeefa M.S., Akshara Raghu, Anamika Sudheer, Shimy Joseph . A Survey on Deepfake Video Detection using Hybrid Multimodal Features. International Journal of Computer Applications. 187, 82 ( Feb 2026), 1-8. DOI=10.5120/ijca2026926440
Abstract
The rapid evolution of deep generative models has enabled the creation of highly realistic deepfake videos, posing significant threats to digital media authenticity, privacy and public trust. Modern deepfake generation techniques based on Generative Adversarial Networks (GANs) [1], autoencoders [2] and neural rendering models [3] can synthesize facial expressions, lip movements and identities with remarkable realism, making manual detection increasingly unreliable. This paper presents a comprehensive survey of deepfake video detection techniques with a focus on hybrid multimodal approaches that integrate spatial, temporal and physiological features [4, 5]. Existing methods based on visual artifacts, temporal inconsistencies, frequency-domain analysis and biological signal extraction such as remote photoplethysmography (rPPG) [5, 10] are systematically reviewed. The survey further examines hierarchical fusion architectures, benchmark datasets [2, 3], evaluation protocols and real-world deployment challenges. Key limitations and open research directions are identified to guide the development of robust, generalizable and real-time deepfake detection systems.
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