Leveraging Advanced Machine Learning Architectures for Robust Deepfake Detection in Digital Facial Imagery

Abstract

The swift progression of artificial intelligence, especially via Generative Adversarial Networks (GANs) and Diffusion Models, has facilitated the production of remarkably authentic "deepfakes" that jeopardize digital security and media authenticity. recent detection techniques often encounter challenges with zero-shot generalization, failing to distinguish between synthetic media generated through non-traditional models or maliciously modified with adversarial noise. This study introduces an effective deepfake detection system assessed using a high-quality custom synthetic dataset. The approach incorporates a deep learning classification model that can achieve 95% accuracy in practical situations, exhibiting considerable robustness against evading strategies like Fast Gradient Sign Method (FGSM) adversarial disruptions. The study expands beyond binary classification by embedding a quantitative aspect into digital forensics through the application of a semantic segmentation method utilizing the FCN-ResNet101 framework. This facilitates the accurate identification of altered areas and the assessment of the percentage of a picture that has been affected. The experimental findings confirm the model's efficiency in both regulated and real-world settings, presenting a scalable approach to enhancing digital trust and delivering comprehensible forensic proof of media manipulation.