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AI-Based Detection of Deepfake Videos

Vidya Sampat Gadhave, Vaishnavi Dinesh Deshkmukh

Abstract:The rapid proliferation of hyper-realistic, AI-generated "deepfake" videos has created significant societal risks, from political disinformation to identity fraud. Current detection methodologies, primarily based on Convolutional Neural Networks (CNNs), struggle to generalize across different forgery methods and are vulnerable to post-processing compression. This paper proposes a novel framework, Hybrid Spatial-Temporal Transformer (HST-Trans), designed to overcome these limitations. The HST-Trans architecture integrates an EfficientNetV2 backbone for capturing micro-level spatial anomalies (like skin texture inconsistencies) with a Vision Transformer (ViT) to model macro-level global dependencies and temporal flickering. Our evaluation on the FaceForensics++ and Celeb-DF v2 datasets demonstrates that this hybrid approach achieves a state-of-the-art accuracy of 98.4% and shows significantly improved robustness against video compression compared to pure CNN models. This research provides a critical step toward reliable, "in-the-wild" deepfake detection.
Keywords:Deepfake Detection, Facial Manipulation, Hybrid Deep Learning, Vision Transformers, Generalization Gap, Digital Forensics.