AI-Driven Framework for Detection of Multimedia Forgeries to Strengthen Digital Evidence Integrity and Cybersecurity

Abstract

Multimedia information, including images, audio, and video, is essential to communication, the presenting of evidence, news reporting, and the formation of public opinion in today's digital environment. But the ease of digital technology has also made media manipulation frighteningly simple, endangering security, trust, and the truth. Forged multimedia can be used to propagate false information, sabotage national cybersecurity, create conflict, and misinterpret the law. The difficulty to accurately confirm the legitimacy of multimedia evidence has grown to be a significant problem for the public, judicial institutions, and law enforcement in Nigeria and throughout the world. This study presents the development of a novel AI-based system designed to detect and highlight manipulated or fake multimedia information with remarkable accuracy, efficiency, and scalability. The framework will perform a passive analysis of digital media and identify minute indications of manipulation, such as frame insertion, duplication, deletion, and other advanced forgery techniques, using deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). With a focus on audio-visual forgery detection, the study will create a robust and adaptable model that has been trained on both artificial and real-world datasets, including content that replicates the local media landscape. This study will assist safeguard Nigeria's digital environment against the spread of dangerous content and false information, in addition to improving the reliability of digital evidence in judicial investigations. Supporting national and international cybersecurity objectives, enhancing the legitimacy of digital justice procedures, and adding to digital forensics policy frameworks are the objectives of this study. In the end, this effort will help people and organizations navigate the digital world with more resilience, trust, and confidence.