This paper proposes a comprehensive reliability assessment framework for deepfake detectors, moving beyond traditional performance metrics. It evaluates five state-of-the-art methods across four crucial pillars: transferability, robustness, interpretability, and computational efficiency, revealing both progress and critical limitations in current detection technologies.