Cybersecurity Governance Model Toward Deepfake Manipulation
Keywords:
Artificial intelligence, Convolutional neural network, Cybersecurity governance, Deepfake detection, MesoNet, Synthetic mediaAbstract
This study developed and evaluated a Cybersecurity Governance Model (CGM) that integrates MesoNet-CNN-based deepfake detection with cybersecurity, data protection, and AI governance requirements. A sequential mixed-methods design combined quantitative deepfake detection with qualitative analysis of governance policies. The MesoNet-4 CNN, trained on the DFDC dataset using OpenCV, MTCNN, and class-weighted Binary Cross-Entropy loss under the Adam optimiser, achieved 94.43% accuracy, 95.2% precision, 93.7% recall, 94.4% F1-score, 0.981 AUC-ROC, and 0.883 MCC on 19,600 frames, outperforming XceptionNet, ResNet-50, and a baseline CNN. Governance requirements were mapped from ISO/IEC 27001:2022, NIST AI RMF, NIST CSF 2.0, GDPR, NDPA 2023, and the EU AI Act. A governance response function, G(x), classified detection probabilities into three tiers using thresholds of 0.41 and 0.72, cutting the governance-relevant missed detection rate by 13% and achieving 87.7% compliance coverage. The findings show that effective deepfake governance requires combining technical detection with structured, auditable policy responses, particularly within the Nigerian context.
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