@Article{cmc.2025.062954,
AUTHOR = {Muhammad Javed, Zhaohui Zhang, Fida Hussain Dahri, Asif Ali Laghari, Martin Krajčík, Ahmad Almadhor},
TITLE = {Real-Time Deepfake Detection via Gaze and Blink Patterns: A Transformer Framework},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {1},
PAGES = {1457--1493},
URL = {http://www.techscience.com/cmc/v85n1/63504},
ISSN = {1546-2226},
ABSTRACT = {Recent advances in artificial intelligence and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Therefore, developing reliable and robust deepfake video detection mechanisms is paramount. This research introduces a novel real-time deepfake video detection framework by analyzing gaze and blink patterns, addressing the spatial-temporal challenges unique to gaze and blink anomalies using the TimeSformer and hybrid Transformer-CNN models. The TimeSformer architecture leverages spatial-temporal attention mechanisms to capture fine-grained blinking intervals and gaze direction anomalies. Compared to state-of-the-art traditional convolutional models like MesoNet and EfficientNet, which primarily focus on global facial features, our approach emphasizes localized eye-region analysis, significantly enhancing detection accuracy. We evaluate our framework on four standard datasets: FaceForensics, CelebDF-V2, DFDC, and FakeAVCeleb. The proposed framework results reveal higher accuracy, with the TimeSformer model achieving accuracies of 97.5%, 96.3%, 95.8%, and 97.1%, and with the hybrid Transformer-CNN model demonstrating accuracies of 92.8%, 91.5%, 90.9%, and 93.2%, on FaceForensics, CelebDF-V2, DFDC, and FakeAVCeleb datasets, respectively, showing robustness in distinguishing manipulated from authentic videos. Our research provides a robust state-of-the-art framework for real-time deepfake video detection. This novel study significantly contributes to video forensics, presenting scalable and accurate real-world application solutions.},
DOI = {10.32604/cmc.2025.062954}
}