International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid advancement of deep learning and generative models has led to the proliferation of highly realistic synthetic media, commonly known as deepfakes. These manipulated images and videos pose significant threats to privacy, security, and information integrity. Detecting deepfakes has thus become a critical area of research. This study explores the role of facial feature analysis in deepfake detection, focusing on the subtle inconsistencies and artifacts that distinguish authentic faces from manipulated ones. The integration of machine learning and computer vision techniques allows for the identification of minute discrepancies that are often imperceptible to the human eye. The public also believes in deepfakes, and in these situations, individuals are unable to distinguish between genuine and fake. The purpose of this research is to determine which is right and which is not. The Facial Feature Analysis and Miniature Pattern Dissimilarity Verification model (FFA-MPDV), which combines meso4 for lightweight forgery detection with a capsule network to improve special feature retention, is part of the suggested model in this study. Unlike traditional deepfake detection methods, which often struggle with subtle image modifications, the proposed FFA. This unique combination significantly improves detection performance, achieving an impressive 97.3% accuracy, setting it apart from current state of-the-art techniques and making it possible to identify which photographs are real and which are fraudulent in a matter of seconds.

Keywords: Deepfake Detection, Facial Feature Analysis, Generative Models, Capsule Networks, Spatial Attention Mechanism, Multi-Scale Feature Extraction, Forgery Detection, Deep Learning, Computer Vision, Security and Privacy.

Downloads: | DOI: 10.17148/IJARCCE.2025.141227