FACIAL FEATURE ANALYSIS FOR DEEPFAKE DETECTION

Abstract: The rapid advancement of artificial intelligence and deep learning technologies has led to the widespread creation of highly realistic synthetic media known as deepfakes. Deepfake content can manipulate facial expressions, voices, and visual appearances in images, videos, and audio recordings, making it increasingly difficult to distinguish between real and fabricated media. Such manipulations pose serious threats to digital security, privacy, and public trust. This project presents an intelligent deepfake detection system that performs facial feature analysis to identify manipulated multimedia content, including images, videos, and audio.

The proposed system utilizes advanced image processing and deep learning techniques to analyse facial characteristics and detect inconsistencies introduced during deepfake generation. The system employs a hybrid deep learning architecture based on Meso4Net and Capsule Network (CapsuleNet) models, which effectively capture both texture- level artifacts and spatial relationships in facial structures. The detection process involves multiple stages including preprocessing, frame extraction from videos, facial landmark detection, feature extraction, and classification to determine whether the input media is real or manipulated.

Keywords: Deepfake, Deep Learning, Capsule Network, Meso4Net, Artificial Intelligence.