Deepfake Image Detection Using Machine Learning

Abstract

The rapid advancement of generative artificial intelligence has significantly increased the creation of highly realistic manipulated images, commonly known as deepfakes. These synthetic images pose serious threats to digital security, privacy, and information authenticity, as they are often indistinguishable from real images through human observation. This growing challenge necessitates the development of automated and reliable detection systems capable of identifying subtle visual inconsistencies in manipulated content.This paper presents a deep learning–based approach for detecting deepfake images using a transfer learning framework. A pretrained convolutional neural network model, MobileNetV2/EfficientNet, is utilized for feature extraction, followed by classification layers to distinguish between real and fake images. The proposed system incorporates image preprocessing techniques such as resizing, normalization, and data augmentation to improve model robustness. The model is trained on benchmark datasets containing both genuine and manipulated images and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.