Facial and Neck Region Analysis for Deepfake Detection Using Remote Photoplethysmography Signal Similarity

Deepfake (DF) involves utilizing artificial intelligence (AI) technology to synthesize or manipulate images, voices, and other human or object data. However, recent times have seen a surge in instances of DF technology misuse, raising concerns about cybercrime and the credibility of manipulated information. The objective of this study is to devise a method that employs remote photoplethysmography (rPPG) biosignals for DF detection. The face was divided into five regions based on landmarks, with automatic extraction performed on the neck region. We conducted rPPG signal extraction from each facial area and the neck region was defined as the ground truth. The five signals extracted from the face were used as inputs to an support vector machine (SVM) model by calculating the euclidean distance between each signal and the signal extracted from the neck region, measuring rPPG signal similarity with five features. Our approach demonstrated robust performance with an area under the curve (AUC) score of 91.2% on the audio-driven dataset and 99.7% on the face swapping generative adversarial network (FSGAN) dataset, even though we only used datasets excluding DF techniques that can be visually identified in Korean DF Detection Dataset (KoDF). Therefore, our research findings demonstrate that similarity features of rPPG signals can be utilized as key features for detecting DFs.