A Multi Modal Deepfake Detection Framework for Social Media Platforms to Counter Internet Scams and Misinformation

This research presents a comprehensive deepfake detection framework specifically designed for social media environments to address the growing threat of internet scams and misinformation campaigns. The study analyzes current deepfake proliferation patterns across major social platforms and develops a detection system that combines facial recognition analysis, speech transcription verification, and linguistic pattern assessment to identify manipulated content in real time. The framework was evaluated using a curated dataset of high fidelity deepfakes sourced from social media ecosystems, achieving detection accuracy of 98.25 percent on machine generated text content and demonstrating robust performance across video and audio manipulations. The findings provide social media platforms, cybersecurity professionals, and policymakers with an implementable solution for identifying synthetic media before it can propagate and cause harm to users through financial scams or reputation damage.