This paper presents a comparative analysis of proactive and passive detection methods for identifying deepfake speech. The authors explore how proactive watermark-based and passive acoustic detection frameworks perform under realistic manipulation scenarios, highlighting trade-offs in robustness, scalability, and detection efficiency.