An Insightful Deep Fake Face Detection System Using AIML

Abstract

The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) has led to the creation of highly realistic deepfake face manipulations, raising serious concerns regarding digital security, misinformation, and identity fraud. This paper presents an insightful deepfake face detection system that utilizes advanced deep learning techniques, particularly Convolutional Neural Networks (CNNs), to effectively identify manipulated facial content in images and videos. The proposed system focuses on detecting subtle inconsistencies in facial features, textures, and spatial patterns that are difficult for humans to perceive. It incorporates preprocessing methods, face extraction algorithms, and optimized classification models, along with techniques such as transfer learning and attention mechanisms to enhance detection accuracy and robustness. The model is trained and evaluated on standard datasets, demonstrating strong performance in terms of accuracy, precision, recall, and F1-score. The system is scalable and suitable for real-world applications such as social media verification, digital forensics, and cybersecurity, contributing to the prevention of deepfake-based threats and ensuring the authenticity of digital media.