The rapid evolution of AI has made deepfakes a growing threat, with thousands of tools enabling attackers to create convincing fake content. This surge highlights the need for reliable detection, especially in legal and forensic contexts. Yet most tools rely on blackbox AI models that offer classification results without explaining their decisions. As global regulations tighten, transparency and interpretability becomes more important.  Regulatory standards in China, EU, and U.S. now require and explainable features to ensure AI decisions are legally and scientifically valid. However, mainstream deepfake detectors still fall short, lacking the explainability needed for decision making. To close this gap, we propose a proof-of-concept facial deepfake detector built around a transparent framework aligned with the regulatory demands and incorporates explainable features.