DeepFake Shield AI-Based Detection Fake Videos and Images in Real Time

Abstract

The availability of deepfake technologies has sparked critical concerns about information authenticity, individual privacy, and cyber security Deepfakes are computer-generated images and videos that have been edited using sophisticated computer methods, rendering them extremely realistic and frequently hard to detect with the naked eye. Deepfake Shield is an approach to detecting forged visual media, including images and videos, through the use of convolutional neural networks (CNNs).It works by analyzing important features of media in a bid to determine whether its genuine or not.

The system is simple to use, and non-experts can check for counterfeit content in real time. It is also simple to incorporate into existing cybersecurity architectures and is light enough and scalable enough to deploy widely. It shows, in early testing, the capacity to detect falsified media with over 90% accuracy on well-used public datasets. Future work on the project will further develop the system with support for a variety of media types, enhance quality of describing predictions, and incorporating IoT-based alerts to react swiftly in actual situations.