A Survey on an Integrated Neural Framework for Real-Time Deepfake Detection across Multimedia to Mitigate Misleading Content

with the rapid proliferation of information across digital media, the assessment of information reliability has become increasingly critical for individuals and societies. Although the concept of deepfakes is not entirely novel, their widespread prevalence has emerged as pressing concern. The influence of deepfakes and disinformation extends from provoking individuals to shaping public opinion, misleading communities, and potentially impacting national stability. A variety of online techniques exits for both the detection and generation of deepfakes. This study, through a systematic literature review, examines automated detection and generation approaches, encompassing methods, frameworks, algorithms, and tools across multiple modalities (Audio, image and video). Furthermore, it explores the applicability of these approaches in diverse contexts to effectively counter the dissemination of deepfakes and propagation of disinformation. In this paper, a system is created that uses a Convolutional Neural Network (CNN) with EfficientNet and LSTM designs to pull out details from each frame. The Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm is used for face detection and alignment in images. It utilizes a series of cascaded convolutional neural networks to efficiently locate faces and facial landmarks. Experimental analysis is performed using the DFDC deepfake detection challenge on Kaggle. These deep learning-based methods are optimized to increase accuracy and decrease training time by using this dataset for training and testing. This study concludes with policy recommendations derived from an analysis of advanced artificial intelligence (AI) methods for deepfake detection and generation in digital media. The review consolidates recent progress in detection and generation frameworks, providing a comprehensive overview of current capabilities. Furthermore, it highlights the potential of AI-driven approaches to improve detection accuracy and support regulatory measures aimed at mitigating the societal risks of deepfakes.