Detecting Deepfake Media with AI and ML

Abstract

Deepfake technology has rapidly advanced, enabling the creation of highly realistic yet manipulated digital media. These artificial videos and images pose significant risks to digital security, misinformation, and identity fraud. Traditional forensic techniques struggle to detect deepfakes effectively due to the increasing sophistication of Generative Adversarial Networks (GANs) and other deep learning-based synthesis methods. The need for a robust, scalable, and automated detection system has become crucial for ensuring media authenticity. This research presents DeepFake Bot, an AI-driven system designed to identify manipulated media with high accuracy. The model integrates Convolutional Neural Networks (CNNs) for spatial analysis and Recurrent Neural Networks (RNNs) for temporal consistency verification. Key detection techniques include eye-blinking pattern analysis, facial texture inconsistency detection, and motion anomaly recognition. The system undergoes extensive training using publicly available deepfake datasets, ensuring its ability to generalize across diverse manipulation techniques. The proposed method is evaluated on large-scale benchmark datasets, including FaceForensics++, Celeb-DF, and the DeepFake Detection Challenge (DFDC) dataset. Experimental results demonstrate that DeepFake Bot achieves 92.4% accuracy, outperforming existing deepfake detection models while maintaining real-time processing efficiency.