AI Anomaly-Based Deepfake Detection Using Customized Mahalanobis Distance and Head Pose with Facial Landmarks

The development of artificial intelligence has inevitably led to the growth of deepfake images, videos, human voices, etc. Deepfake detection is mandatory, especially when used for unethical and illegal purposes. This study presents a novel approach to image deepfake detection by introducing the Custom-Made Facial Recognition Algorithm (CMFRA), which employs four distinct features to differentiate between authentic and deepfake images. The proposed method combines facial landmark detection with advanced statistical analysis, integrating mean Mahalanobis distance and three head pose coordinates (yaw, pitch, and roll). The landmarks are extracted using the Google Vision API. This multi-feature approach assesses facial structure and orientation, capturing subtle inconsistencies indicative of deepfake manipulations. A key innovation of this work is introducing the mean Mahalanobis distance as a core feature for quantifying spatial relationships between facial landmarks. The research also emphasizes anomaly analysis by focusing solely on authentic facial data to establish a baseline for natural facial characteristics. The anomaly detection model recognizes when a face is modified without extensive training on deepfake samples. The process is implemented by analyzing deviations from this established pattern. The CMFRA demonstrated a detection accuracy of 90%. The proposed algorithm distinguishes between authentic and deepfake images under varied conditions.