Video Integrity Detection with Deep Learning
R Chandru, Antony Vincent R, R. Priscilla · 2024
In today's digital age, ensuring information integrity against deepfakes is essential. This project addresses this challenge by developing a scalable video integrity detection system through the fusion of convolutional neural networks (CNNs) for analyzing video frames and recurrent neural networks (RNNs) for temporal behavior. The model incorporates advanced architectures designed to capture subtle inconsistencies and manipulation artifacts, leveraging cutting-edge techniques for enhanced detection compared to traditional methods. Its adaptability is enhanced through the utilization of domain adaptation and adversarial training strategies, ensuring durability against evolving manipulation techniques. Designed for real-time capabilities, the system is optimized for efficient inference, enabling proactive identification and mitigation of manipulated content on online platforms. Expected outcomes include achieving high accuracy in discriminating real from manipulated videos, promoting improved trust and transparency in the digital media landscape. By providing a reliable means to verify the integrity of video content, the system contributes to a more trustworthy and secure digital ecosystem. This multi-modal fusion approach, coupled with advanced architectures, effectively addresses the necessity for identifying and controlling the spread of manipulated video content.