Enhancing Cybersecurity Defenses Against AI-Generated Deepfake Videos: A Framework for Real-Time Detection and Mitigation

Abstract

Artificial intelligence has increased quickly, allowing the production of highly realistic deepfake videos, which are extremely dangerous to cybersecurity, privacy, and putting trust in people. Conventional detector control systems do not necessarily work in real-time and systems are susceptible to malicious use. The research will suggest a holistic approach to the real-time detection and mitigation of AI-generated deepfake videos. The framework uses sophisticated machine learning and deep learning models, such as convolutional neural networks (CNNs) and transformer based ones, with multi-modal visual, audio, and metadata modeling. The suggested system is built to work hand in hand with the cybersecurity monitoring systems, allowing to perform automated threat detection and response, including flagging of content and verification of the source. Through the framework, experimental analysis has revealed that the framework can attain high detection ability with minimal latency, providing a scalable platform to digital environment measures against new deepfake and fraudsters. The research will help in the creation of more resilient cybersecurity defences as well as offer practical information to policymakers, platform proprietors and security experts.