Hybrid Convolutional Model for Multimodal Deepfake Detection

Abstract

Artificial Intelligence (AI) is a fast-developing area of computer science that creates systems able to think and learn like humans. It is used in virtual assistants, self-driving cars, healthcare, and recommendation tools. The main goal of AI is to simplify tasks, improve efficiency, and drive innovation across industries. In existing system, conventional machine learning models approaches have been developed to identify manipulated media, with a major focus on audio alterations. This method employ techniques such as pattern recognition, feature extraction, and machine learning models to analyze signals and detect inconsistencies. They are widely used to identify tampered or falsified content, contributing to the early detection of manipulated media across various digital platforms. Even though they provided solution but it has some problem regarding their effectiveness in critical applications such as media verification, legal investigations, and identity protection, where accuracy and trust are vital. To address these limitations, we propose a hybrid AI model that integrates image, audio, and video analysis to improve accuracy, robustness, and adaptability. By training on diverse datasets, the model ensures real-time and dependable detection, making it highly suitable for sensitive domains that demand reliable verification.