With the rapid advancement of face recognition systems, security threats posed by adversarial attacks have become increasingly sophisticated. This study presents a novel adversarial methodology for crafting dual-identity face impersonations using Generative Adversarial Networks (GANs). The proposed framework generates synthetic facial images that simultaneously resemble two distinct target identities, thereby enabling high-confidence impersonation across multiple recognition systems. Leveraging a multi-objective loss function, the generator is trained to optimize both identity similarity scores and realism metrics while evading detection from spoofing and liveness classifiers. Extensive evaluations on benchmark datasets such as LFW and CASIA-WebFace demonstrate the effectiveness of the method in deceiving state-of-the-art face verification models with minimal perceptual distortion. The research highlights the vulnerabilities of current biometric systems and underscores the urgent need for robust defense mechanisms against such dual-target adversarial threats.