An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating fea...

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Bibliographic Details
Published in:Electronics (Basel) 2024-06, Vol.13 (12), p.2398
Main Authors: Alhaji, Hanan Saleh, Celik, Yuksel, Goel, Sanjay
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Ant colony optimization
Automation
Datasets
Deception
Deep learning
Deepfake
False information
Identity theft
Image manipulation
Mathematical optimization
Methods
Neural networks
Particle swarm optimization
Privacy
Privacy, Right of
Propaganda
Real time
Robustness
Theft
Video
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Summary:The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13122398