What question did this study set out to answer?

This research aims to develop a reliable system for detecting deepfake videos using a hybrid architecture.

May 10, 2026Open Access

DeepFake Video Detection Using Hybrid ResNet18-BiLSTM Architecture with Spatiotemporal Learning and Real-Time Forensic Analysis

Key Points

This research aims to develop a reliable system for detecting deepfake videos using a hybrid architecture.
Developed a hybrid model integrating ResNet18 and BiLSTM for spatiotemporal learning.
Evaluated the model using FaceForensics++ and CelebDF datasets.
Measured performance through accuracy and AUC metrics.
Achieved 96.30% accuracy in detecting deepfake videos.
Obtained an AUC score of 0.9922, indicating strong performance.
Demonstrated robustness for real-time detection applications.

Abstract

This research presents a hybrid deepfake video detection framework integrating ResNet18 and BiLSTM architectures for robust spatiotemporal forensic analysis. The proposed system combines spatial feature extraction and temporal sequence modeling to detect manipulated videos with high accuracy. Experimental evaluation was conducted using FaceForensics++ and CelebDF datasets. The proposed model achieved 96.30% accuracy and 0.9922 AUC score, demonstrating strong robustness and generalization capability for real-time deepfake detection applications in cybersecurity and multimedia forensics.

DeepFake Video Detection Using Hybrid ResNet18-BiLSTM Architecture with Spatiotemporal Learning and Real-Time Forensic Analysis

Key Points

Abstract

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