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.
References
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nießner, M. “FaceForensics++: Learning to detect manipulated facial images,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1–11, 2019.
Li, Y., Yang, X., Sun, P., Qi, H., and Lyu, S. “Celeb-DF: A large-scale challenging dataset for DeepFake forensics,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3204–3213, 2020. DOI: 10.1109/CVPR42600.2020.00327.
Jiang, L., Li, R., Wu, W., Qian, C., and Loy, C. C. “DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2886–2895, 2020. DOI: 10.1109/CVPR42600.2020.00296.
Zi, B., Chang, M., Chen, J., Ma, X., and Jiang, Y.-G. “WildDeepfake: A challenging real-world dataset for deepfake detection,” in Proc. ACM International Conference on Multimedia (ACM MM), pp. 2382–2390, 2020.
Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. “MesoNet: A compact facial video forgery detection network,” arXiv preprint arXiv:1809.00888, 2018.
Li, Y., and Lyu, S. “Exposing DeepFake videos by detecting face warping artifacts,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 46–52, 2019.
Li, Y., Chang, M.-C., and Lyu, S. “In ictu oculi: Exposing AI generated fake face videos by detecting eye blinking,” in Proc. IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7, 2018.
Nguyen, H. H., Yamagishi, J., and Echizen, I. “Capsule-Forensics: Using capsule networks to detect forged images and videos,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2307–2311, 2019.
Dang, H., Liu, F., Stehouwer, J., Liu, X., and Jain, A. K. “On the detection of digital face manipulation,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5780–5789, 2020.
Qian, Y., Yin, G., Sheng, L., Chen, Z., and Shao, J. “Thinking in frequency: Face forgery detection by mining frequency-aware clues,” in Proc. European Conference on Computer Vision (ECCV), pp. 86–103, 2020.
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., and Xia, W. “Learning self-consistency for deepfake detection,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15023–15033, 2021.
Luo, Y., Zhang, Y., Yan, J., and Liu, W. “Generalizing face forgery detection with high-frequency features,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16317–16326, 2021.
Cozzolino, D., Rössler, A., Thies, J., Nießner, M., and Verdoliva, L. “ID-Reveal: Identity-aware DeepFake video detection,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15108–15117, 2021.
Kwon, P., You, J., Nam, G., Park, S., and Chae, G. “KoDF: A large-scale Korean DeepFake detection dataset,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10744–10753, 2021.
Le, T.-N., Nguyen, H. H., Yamagishi, J., and Echizen, I. “OpenForensics: Large-scale challenging dataset for multi-face forgery detection and segmentation in-the-wild,” in Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10117–10127, 2021.
Shiohara, K., and Yamasaki, T. “Detecting deepfakes with self-blended images,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18720–18729, 2022.
Dong, X., Bao, J., Chen, D., Zhang, T., Zhang, W., Yu, N., Chen, D., Wen, F., and Guo, B. “Protecting celebrities from DeepFake with identity consistency transformer,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9468–9478, 2022.
Patel, Y., Tanwar, S., Bhattacharya, P., Gupta, R., Alsuwian, T., Davidson, I. E., and Mazibuko, T. F. “An improved dense CNN architecture for deepfake image detection,” IEEE Access, vol. 11, pp. 22081–22095, 2023.
Mirsky, Y., and Lee, W. “The creation and detection of deepfakes: A survey,” ACM Computing Surveys, vol. 54, no. 1, pp. 1–41, 2021.
Nanda, S. K., Ghai, D., and Ingole, P. “Analysis of video forensics system for detection of gun, mask and anomaly using soft computing techniques,” Journal of Cyber Security and Mobility, vol. 11, no. 4, pp. 549–574, 2022.
Haddad, N. M., Mustafa, M. S., Salih, H. S., Jaber, M. M., and Ali, M. H. “Analysis of the security of internet of multimedia things in wireless environment,” Journal of Cyber Security and Mobility, vol. 13, no. 1, pp. 161–192, 2024.
Xiao, P. “Network malware detection using deep learning network analysis,” Journal of Cyber Security and Mobility, vol. 13, no. 1, pp. 27–52, 2024.
Xiao, P. “Malware cyber threat intelligence system for Internet of Things (IoT) using machine learning,” Journal of Cyber Security and Mobility, vol. 13, no. 1, pp. 53–90, 2024.
Hussain, S. S., Razak, M. F. A., and Firdaus, A. “Deep learning based hybrid analysis of malware detection and classification: A recent review,” Journal of Cyber Security and Mobility, vol. 13, no. 1, pp. 91–134, 2024.
Singh, V. K., Sivashankar, D., Kundan, K., and Kumari, S. “An efficient intrusion detection and prevention system for DDOS attack in WSN using SS-LSACNN and TCSLR,” Journal of Cyber Security and Mobility, vol. 13, no. 1, pp. 135–160, 2024.
Deng, Y. “Design of industrial IoT intrusion security detection system based on LightGBM feature algorithm and multi-layer perception network,” Journal of Cyber Security and Mobility, vol. 13, no. 2, pp. 327–348, 2024.
Zhang, J., Zou, H., Zeng, Z., Xu, W., and Jiang, J. “Feasibility of using Seq-GAN model in vulnerability detection of industrial control protocols,” Journal of Cyber Security and Mobility, vol. 13, no. 3, pp. 393–416, 2024.
Li, W. “Construction and analysis of QPSO-LSTM model in network security situation prediction,” Journal of Cyber Security and Mobility, vol. 13, no. 3, pp. 417–438, 2024.
Lu, Y. “Security and privacy of Internet of Things: A review of challenges and solutions,” Journal of Cyber Security and Mobility, vol. 12, no. 6, pp. 813–844, 2023.
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