Published Paper Details:

The rapid advancement of artificial intelligence (AI) has led to the emergence of deepfakes--synthetic media in which a person's likeness is convincingly replaced with another's. While these AI-generated manipulations have potential applications in entertainment and education, they also pose significant risks to privacy, misinformation, and digital security. To combat this growing threat, AI-driven deepfake detection systems have been developed, leveraging machine learning, computer vision, and neural networks. This paper explores the current state of AI technologies in detecting deepfakes, including the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. Techniques such as facial landmark analysis, inconsistencies in blinking patterns, and frequency domain artifacts are examined. Moreover, the paper highlights the role of large datasets, adversarial training, and explainable AI in improving detection accuracy. Despite significant progress, challenges remain in generalizing detection models across diverse datasets and evolving deepfake techniques. The ongoing arms race between deepfake generation and detection necessitates continued innovation and collaboration across academia, industry, and policy-making spheres.