Detection of Fake Video Authenticity Using Time Analysis

Detection of Fake Video Authenticity Using Time Analysis In recent years, the rapid growth of digital media and artificial intelligence has led to the widespread creation of manipulated videos, commonly known as deepfakes. These fake videos pose serious threats to security, privacy, and information integrity. This project focuses on detecting the authenticity of videos by analyzing temporal inconsistencies using time-based techniques. The proposed system examines the sequence of frames in a video and identifies irregularities in timing, motion continuity, and frame transitions. Genuine videos typically maintain consistent temporal patterns, whereas fake or manipulated videos often exhibit abnormalities such as frame duplication, missing frames, or unnatural motion flow. By applying time analysis algorithms, the system can effectively distinguish between real and fake video content. The methodology involves extracting video frames, analyzing frame intervals, and evaluating temporal features using computational techniques. The system may also incorporate statistical analysis or machine learning models to improve detection accuracy. Experimental results demonstrate that the proposed approach can successfully identify manipulated videos with improved reliability. This project contributes to enhancing digital media verification systems and can be applied in areas such as social media monitoring, forensic analysis, and cybersecurity to prevent the spread of misinformation.