Advanced Deepfake Detection and Classification Using Deep Learning

Deepfakes, synthetic media created using deep learning techniques, have emerged as a significant concern due to their potential for misuse in various domains, including social media, politics, and law enforcement. The ability to generate highly realistic and convincing fake content has raised serious ethical and legal implications. The research proposes a robust and efficient deepfake detection system based on MobileNetV2, a lightweight cnn architecture which offers several advantages, including computational efficiency, high accuracy, and generalizability, making it suitable for deepfake detection tasks.The approach leverages a comprehensive dataset of over 120,000 real and AI-generated face images to train the deepfake detection model. This extensive available public dataset allows the study to develop a robust and generalizable model that can accurately differentiate real and fake images.The study is conducted thorough performance evaluations like Precision, Recall, F1-Scores to assess the accuracy and efficiency of the proposed system & compared the model with other state of the art works. The results demonstrate that the MobileNetV2-based model achieves high detection accuracy of 95.46%, making it suitable for real-world applications and depicting the model’s high potential for deepfake image detection and classification problem.