Abstract: In today's rapidly evolving digital landscape, the security and integrity of software applications are paramount. As technology progresses, so do the intricacies of cyber threats, highlighting the critical importance of identifying and resolving vulnerabilities. Addressing this need, we present "3D Face Reconstruction and Deepfake Detection," a project marking a significant advancement in the fields of computer vision and deep learning. We employ Volumetric Convolutional Neural Networks (CNNs) to reconstruct 3D facial models with precision and accuracy, leveraging the feed-forward properties of CNNs to ensure stability and efficiency. This innovative approach enhances the quality of 3D reconstructions, showcasing the potential of deep learning in solving complex real-world problems. Equally important, our project integrates an advanced deepfake detection system using MesoNet, which efficiently identifies synthetic facial images and ensures the authenticity of the reconstructed 3D models. By leveraging a custom dataset that combines various standard datasets, our deepfake detection model achieves high accuracy and robustness, minimizing false positives and negatives. The dual focus on 3D face reconstruction and deepfake detection exemplifies the power of machine learning in capturing intricate facial features and structures while simultaneously safeguarding against digital threats. "3D Face Reconstruction and Deepfake Detection" represents a pivotal step at the intersection of technology and innovation, redefining the processes of 3D face reconstruction and deepfake detection, and making a significant contribution to the fields of computer vision, digital security, and 3D modeling.
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DOI:
10.17148/IJARCCE.2024.13588
