Paper Contents
Abstract
Vision guard is an intelligent system designed to address the growing threat of AI-generated synthetic videos, such as deepfakes and digitally manipulated footage. As generative technologies become more sophisticated, distinguishing real videos from synthetic ones has become increasingly challenging, raising serious concerns in digital forensics, media integrity, and public trust. Vision Guard leverages deep learning modelsspecifically convolutional neural networks CNN combined with temporal and spatial feature analysisto identify subtle inconsistencies in facial movements, lighting, texture, and frame transitions that are often present in synthetic videos. A curated dataset of real and synthetic content was used to train the system, resulting in high classification accuracy and robust generalization across different video sources. To enhance interpretability, the system includes attention maps that highlight regions most influential to the models decisions. Vision Guard provides a scalable and efficient solution for real-time video verification, empowering platforms and users to detect visual misinformation. This tool plays a crucial role in safeguarding digital content authenticity in an era where visual deception is both accessible and increasingly convincing.Keywords Vision Guard, Deepfake Detection,Machine Learning, Flask Application, Video, Forensics, Real-time.
Copyright
Copyright © 2025 Subanesh B . This is an open access article distributed under the Creative Commons Attribution License.
