Mitigating Adversarial Vulnerabilities in Deep Learning-Based Face Recognition Using Stacked Attention Residual GAN and Fire Hawk Optimization

Abstract

In computer vision and pattern recognition tasks, deep learning models are widely used, especially in face recognition systems. Even with their excellent performance, these models are still susceptible to a variety of adversarial manipulations, such as blur, additive noise, translation, flipping, scaling, rotation, and changes in illumination. Furthermore, some architectures might experience optimization problems like vanishing gradients, which would further impair the stability of the model. In order to provide robust face verification under adversarial attack, Optimized Deep Learning–based Adversarial Defense Mechanism (ODL-ADM) is proposed in this work. It projects adversarial samples into an immune feature space. A Learnable Convolutional Principal Component Network (LCPCN) is incorporated into the framework to create a representation space that is both discriminative and resistant to perturbations. Adversarially corrupted facial images are suppressed and reconstructed using a Stacked Attention-based Residual Generative Adversarial Network (SARGAN). Accurate identity recognition is achieved by an Improved Cross-Triple MobileNetV1 architecture after perturbation removal. Enhanced Fire Hawk Optimization (EFHO) is used for performance maximization and parameter tuning to further improve recognition performance. Following image reconstruction and adversarial perturbation removal, the suggested model achieves a 98% face recognition accuracy.