Development of ML-Based Solution for Detection of Deepfake Face-Swap Videos

Abstract

Deepfake technology, driven by advancements in deep learning and generative models, enables highly realistic manipulation of facial appearances in videos, often through face-swapping techniques. While such methods have potential in entertainment and creative applications, they also pose serious threats to privacy, trust, and information integrity. This paper presents the development of a machine learning (ML)-based system for detecting face-swap deepfake videos. The proposed approach employs video preprocessing, frame extraction, and facial region isolation, followed by feature extraction using a deep convolutional neural network (ResNeXt). Temporal consistency is analyzed with a Long Short-Term Memory (LSTM) network to capture sequential artifacts. Experimental results demonstrate the system’s ability to distinguish real and fake videos with high accuracy, contributing to digital forensics and misinformation mitigation efforts.[1]