Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition

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.