Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
Han‐Soo Kim · Applied Sciences · 2025
Advancements in science, technology, and computer engineering have significantly influenced biometric identification systems, particularly facial recognition. However, these systems are increasingly vulnerable to sophisticated forgery techniques. This study presents a novel deep learning framework optimized for texture analysis to detect facial forgeries effectively. The proposed method leverages high-frequency texture features, such as roughness, color variation, and randomness, which are more challenging to replicate than specific facial features. The network employs a shallow architecture with wide feature maps to enhance efficiency and precision. Furthermore, a binary classification approach combined with supervised contrastive learning addresses data imbalance and strengthens generalization capabilities. Experimental results, conducted on three benchmark datasets (CASIA-FASD, CelebA-Spoof, and NIA-ILD), demonstrate the model’s robustness, achieving an Average Classification Error Rate (ACER) of approximately 0.06, significantly outperforming existing methods. This approach ensures practical applicability for real-time biometric systems, providing a reliable and efficient solution for forgery detection.