Ai-based Real Time Deep Fake Detection

Abstract

The Rapid Advancement Of Deep Learning And Generative Models, Particularly Generative Adversarial Networks (GANs), Has Enabled The Creation Of Highly Realistic Synthetic Media Known As Deepfakes. These Manipulated Media Forms, Including Images, Videos, Audio, And Text, Pose Significant Threats To Digital Trust, Cybersecurity, And Social Stability. Deepfakes Are Increasingly Used For Misinformation Campaigns, Identity Theft, Political Manipulation, And Financial Fraud, Making Their Detection A Critical Research Challenge. This Paper Proposes A Multimodal Deepfake Detection System That Integrates Advanced Artificial Intelligence Techniques To Analyze And Classify Content Across Multiple Data Modalities. The System Employs BERT-based Natural Language Processing (NLP) For Text Analysis, Convolutional Neural Networks (CNNs) For Image And Audio Classification, And Long Short-Term Memory (LSTM) Networks For Temporal Video Analysis. The Proposed System Is Evaluated Using Benchmark Datasets Such As Celeb-DF, FaceForensics++, And ASVspoof, Achieving High Accuracy Across All Modalities. Furthermore, The System Is Implemented As A Web-based Platform That Enables Real-time Detection Of Deepfake Content. The Results Demonstrate That The Proposed Approach Significantly Improves Detection Performance And Provides A Scalable Solution For Combating Misinformation In Digital Ecosystems.

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