The rise of deepfake generation techniques poses a critical challenge to information authenticity and online trust. Conventional detection strategies often fail against advanced generative models that convincingly reproduce facial behavior. This study introduces a cryptographic framework integrated with Public Key Infrastructure (PKI) for deepfake identification using facial micro-expression cues. PKI ensures data integrity, verification, and non-repudiation, while micro-expression analysis captures minute facial variations that synthetic algorithms struggle to replicate. The combined approach employs cryptographic validation alongside machine learning-based behavioral analysis, resulting in stronger defense against adversarial manipulation and enhanced reliability in digital forensics. Performance testing with publicly available deepfake datasets confirms that the framework achieves superior accuracy, robustness, and efficiency compared to existing methods. The findings emphasize the effectiveness of combining cryptographic assurance with biometric micro-expression analysis to build a scalable and secure ecosystem for detecting manipulated media.