Unmasking the Rise of Deepfakes: A Machine Learning Approach to Detection and Social Media Trend Analysis
Year: 2025
Type: article
Abstract: The increasing prevalence of deepfake videos poses significant threats to information integrity, political stability, and public trust.This study presents a dual-faceted approach: (1) developing a machine learning model for detecting deepfake videos using visual features extracted from benchmark datasets, and (2) conducting a trend analysis of deepfake content dissemination on social media platforms such as YouTube and Twitter (now known as X).Conducted using the FaceForensics++ dataset and metadata from over 2,000 social media posts collected between 2018 and 2024, this study used a fine-tuned Xception model and natural language techniques.Key findings indicate a post-2020 surge in politically motivated deepfakes and platform-specific propagation patterns.It is recommended that stakeholders implement real-time detection and awareness tools to mitigate social impact.
DOI: 10.59765/sct5wr
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409856604