DeepFake Image Detection

The proliferation of deepfake technology represents a significant challenge to the integrity of digital media, raising profound concerns across political, social, and cybersecurity domains. As synthetic media, deepfakes leverage advanced artificial intelligence to create hyper-realistic images and videos that can convincingly depict events or statements that never occurred. This research presents the development of a robust and effective deepfake detection system utilizing a Convolutional Neural Network (CNN) architecture. The proposed system is designed to overcome the limitations of traditional forensic methods by identifying subtle, intricate anomalies that are often imperceptible to the human eye. The model was trained on an extensive dataset of both authentic and manipulated media, achieving a notable detection accuracy of 96%. Developed using Python, Colab for training, and Flask for real-time deployment, the system offers a practical, scalable tool with vital applications in content moderation, media verification, and cybersecurity. The project demonstrates a successful application of advanced machine learning to a pressing societal problem, contributing to the broader effort to safeguard digital trust and combat the malicious use of synthetic media.