Research Paper on AI-Driven Detection of Fake News in Non-Textual Content

In today's information ecosystems, there are serious issues due to the growing frequency of fake news in multimedia content, which includes images, videos, and audio. Non-textual content is still largely unexplored, despite the encouraging outcomes AI-driven algorithms have shown in identifying fake news in textual content. This work offers an extensive analysis of existing AI techniques for identifying false information in non-textual media, such as audio forgeries, modified photos, and deep fake movies. We hope to highlight the opportunities and constraints that currently exist in the field by examining cutting-edge methods like convolutional neural networks (CNNs), deep learning, and natural language processing (NLP) when paired with visual data. Additionally, we suggest that collaborative learning and multi-modal AI techniques could lead to advances. In the final section of the paper, ethical issues and potential research avenues for AI-driven false news detection in non-textual content are discussed.