Deepfake Detection and Media Manipulation Using Hybrid AI Models

Abstract

Through the fast development of generative AI, it is now possible to create very realistic -deepfake tangibles, such as images, video, and audio, threatening the digital trust, media integrity, and cybersecurity. The article shows one of the instances of Hybrid AI-Based Multimodal Deepfake Detection System, analysis of images, video, and audio are provided in a single fusion model, which is beneficial to the check of high accuracy and power of detection. The system denotes dedicated modules of analysis of all the modalities like metadata validation and structural anomaly in images, temporal and frame based analysis in videos and acoustic pattern recognition in audio. The result of these modules is recombined with weighted hybrid decision fusion technique, to provide a synthesized output of detection. This hybrid method is less prone to false positive compared to its single-model counterpart and also more precise as cross-modal evidence is used. It was designed in a modular framework using Streamlit and enabled it to scale because further developments were enabled to use improved deep learning models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based networks. It can be determined that unified hybrid methods have a better protection against compression artifacts, adversarial manipulations and data variability than the existing single-modality and partially hybrid methods. The findings indicate that multimodal hybrid reasoning is quite helpful in the improvement of the detection performance in various media manipulation. The article offers a far more adaptable and scalable structure of establishing resilient systems of deepfake detector that could keep up with the developing code of generative code and more advanced altered media techniques.