A Hybrid Approach for Deep Fake Detection Using Deep Learning Algorithm

Deep Fake technology has emerged as a potent tool for generating highly realistic synthetic media, raising concerns about its potential misuse and impact on society. This paper presents an innovative approach to address the growing threat of Deep Fake proliferation through a robust detection system. Leveraging the power of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), our proposed framework analyzes intricate patterns and temporal dependencies within multimedia content to distinguish authentic from manipulated media. Deep Fakes, which utilize advanced machine learning algorithms to superimpose one person’s likeness onto another’s, pose significant challenges to various sectors, including politics, entertainment, and security. The proposed model aims to mitigate the adverse effects of Deep Fakes by providing an effective and scalable solution for detection. The integration of CNN and RNN enables our system to capture both spatial and temporal features, enhancing its accuracy in identifying manipulated content. As Deep Fake threats continue to evolve, our approach stands as a pivotal advancement in the ongoing effort to safeguard the integrity of digital media and protect against the potential consequences of widespread misinformation and deception. Specifically, our framework employs CNNs to extract spatial features from individual frames of multimedia content, enabling the detection of subtle visual artifacts indicative of manipulation. Additionally, RNNs are utilized to analyze temporal dependencies across frames, allowing the model to discern inconsistencies in motion and facial expressions that may signal the presence of Deep Fake manipulation.