Robust Multimodal Deepfake Forensics for Digital Human Identity Protection in Mobile Multimedia Security

Abstract

Deepfake forgeries pose increasing risks to digital identity protection, media integrity, and forensic reliability in mobile multimedia environments. Although recent detection methods have shown promising results on benchmark datasets, their performance often degrades under cross-domain distribution shifts and real-world perturbations, particularly in digital-human scenarios with complex appearance and motion patterns. To address this issue, this paper proposes a robust multimodal deepfake forensics framework for digital human identity protection. The proposed method jointly exploits visual, audio, and motion cues and further enhances deployment robustness through perturbation-aware learning, adversarial robustness enhancement, and domain-specific adaptation. Experimental results show that the EfficientNet-based implementation achieves ACC/F1-score/AUC values of 0.9245/0.9246/0.9728 on the benchmark setting. Under cross-dataset evaluation, the proposed framework obtains ACC/F1-score/AUC values of 0.8612/0.8729/0.9285 on Celeb-DFv2 and 0.8346/0.8481/0.9043 on WildDeepfake. After opera-domain fine-tuning, performance on OperaDeepfake improves from 0.8415/0.8568/0.9017 to 0.9317/0.9385/0.9781. Under five perturbation settings, the proposed robustness-oriented training strategy improves the F1-score by 0.0662-0.0909 compared with the baseline. These quantitative results demonstrate that the proposed framework provides a practical and robust solution for deepfake forensics, digital human identity protection, and mobile multimedia security applications.