Fake Face Image Detection for Digital Forensics Using Deep Learning Techniques

Abstract

The rapid advancement of artificial intelligence and image synthesis technologies has led to the widespread creation of manipulated facial images, posing significant challenges to digital security, privacy, and forensic investigations. Fake face images generated through techniques such as deepfakes, face swapping, and image editing can be exploited for identity theft, misinformation, fraud, and cybercrime. Consequently, the development of reliable methods for detecting forged facial images has become a critical area of research in digital forensics. This study presents a deep learning-based approach for fake face image detection aimed at enhancing the integrity and authenticity verification process in digital forensic applications. The proposed system utilizes Convolutional Neural Networks (CNNs) and advanced feature extraction techniques to automatically distinguish between genuine and manipulated facial images. A publicly available dataset containing both authentic and fake face images is employed for training, validation, and testing purposes. Data preprocessing methods, including image normalization, augmentation, and feature enhancement, are incorporated to improve model robustness and generalization capabilities. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that deep learning techniques can effectively identify subtle artefacts and inconsistencies introduced during image manipulation, achieving high detection accuracy. The findings of this study contribute to the growing field of digital forensics by providing an intelligent framework capable of supporting investigators, law enforcement agencies, and cybersecurity professionals in combating image forgery and ensuring the authenticity of digital media.