Authentic Vision: Deepfake Detection for Images

This study presents a Deepfake Detection System designed to combat the challenges posed by synthetic media generated through advanced deep learning techniques. Leveraging Convolutional Neural Networks (CNNs) and machine learning methodologies, the system identifies and distinguishes deepfake content from authentic media. By analyzing facial inconsistencies, artifacts, and patterns in video and image data, the system aims to provide a robust and scalable solution for detecting manipulated media. The proposed framework incorporates pre-trained models, fine-tuned on diverse datasets of both deepfake and authentic samples, ensuring high detection accuracy. This system addresses the growing societal and ethical concerns associated with deepfake technologies, including misinformation, fraud, and privacy violations.