AI-based systems for detecting deepfake attacks

Abstract

Deepfake technology leverages artificial intelligence (AI) to generate highly realistic fake media, posing significant threats to cybersecurity, misinformation, and privacy. This paper explores AI-based methodologies for detecting deepfake attacks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. We discuss challenges in deepfake detection, dataset availability, and real-world applications. Furthermore, we evaluate current state-of-the-art approaches and propose an optimized deepfake detection system leveraging multi-modal AI techniques. Experimental results demonstrate the efficacy of our approach in identifying deepfake content with high accuracy.