Biometric authentication systems, such as facial and voice recognition, are increasingly prevalent due to their convenience and enhanced security. However, the emergence of DeepFake attacks, which leverage artificial intelligence to create highly realistic images, videos, and audio recordings, poses a significant threat to the integrity of these systems. This paper proposes an innovative hybrid approach for detecting and responding to DeepFake attacks in biometric authentication systems. The method combines machine learning techniques to identify visual or auditory alterations with spectral analysis to detect inconsistencies in artificially generated biometric signals. This approach aims to enhance the robustness of authentication systems by enabling early detection and effective response to identity spoofing attempts via DeepFake. Experimental results demonstrate that this hybrid solution offers superior performance in detection and resilience against increasingly sophisticated attacks, thereby laying the groundwork for reinforced security in biometric systems in the era of DeepFakes.