Year: 2025
Type: article
Abstract: : Deepfake technology, particularly face-swap manipulation, has raised significant concerns regarding media authenticity andsecurity. This paper presents"FaceSwapExposed," which is an innovative artificialintelligence and machine learning framework designed to detect face swap deepfakes with high accuracy. Our approachutilizesadual-branch convolutional neural network to analyze both high- and low-frequency facial features, enabling the robust identification of subtle artifacts introduced during face swaps. Comprehensive experiments on multiple benchmark datasets demonstrated that our method outperformed existing techniques, achieving a detection accuracy exceeding 95%. The model was trained using advanced data augmentation and regularization strategiestoensurereliabilityacrossvarious lighting conditions and resolutions. Our results underscore the potential of tailored deep learning models for mitigatingdeepfake proliferation. This research not only contributes to improved deepfake detection butalsoprovidesafoundationforfuturework in developing real-time and scalable authenticity verification systems. Our system exhibits promising capabilities in diverse scenarios.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409430075