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.

Remote photoplethysmography (rPPG) extracts physiological signals from facial videos by analyzing subtle skin color variations caused by blood flow. While this technology enables contactless health monitoring, it also raises privacy concerns because facial videos reveal both identity and sensitive biometric information. Existing privacy-preserving techniques, such as blurring or pixelation, degrade visual quality and are unsuitable for practical rPPG applications. This paper presents rePPG, a framework that inserts a desired rPPG signal into facial videos while preserving the original facial appearance. The proposed method disentangles facial appearance and physiological features, enabling replacement of the physiological signal without altering facial identity or visual quality. Skin segmentation restricts modifications to skin regions, and a cycle-consistency mechanism ensures that the injected rPPG signal can be reliably recovered from the generated video. Importantly, the extracted rPPG signals are evaluated against the injected target physiological signals rather than the subject’s original physiological state, ensuring that the evaluation measures signal rewriting accuracy. Experiments on the PURE and UBFC datasets show that rePPG successfully embeds target PPG signals, achieving 1.10 BPM MAE and 95.00% PTE6 on PURE while preserving visual quality (PSNR 24.61 dB, SSIM 0.638). Heart rate metrics are computed using a 5-second temporal window to ensure a consistent evaluation protocol.

This study investigates the effects of heart rate and arm tremor on performance in elite archery, using non-contact physiological monitoring from real Olympic competition footage. A total of 50 video segments were extracted from publicly available international broadcasts, comprising athletes of various backgrounds. From these, heart rate signals were estimated via remote photoplethysmography (rPPG) from facial regions, and micro-movements were quantified from right and left arm regions using feature point tracking. Ordinal logistic regression was employed to evaluate the relationship between biometric variables and archery scores (10, 9, ≤8 points). Results showed that elevated heart rate ($\beta$ = −0.1166; $p<$ 0.001) and greater right-arm movement ($\beta$ = −6.1747; p = 0.008) were significantly associated with lower scores. Athletes scoring 10 points exhibited significantly lower heart rate ($p<$ 0.001) and reduced right-arm tremor (p = 0.010) compared to others. These findings support the hypothesis that physiological arousal and biomechanical instability impair performance, and they further demonstrate the feasibility of contactless monitoring in real competition environments. The proposed method enables objective, in-game performance evaluation and supports the development of personalized training systems for precision sports.

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within $\pm 6$–$12\%$ with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions.