[1] A. Malik, M. Kuribayashi, S. M. Abdullahi, and A.N. Khan, “DeepFake detection for human face images and videos: A survey,”in IEEE Access, vol. 10, pp. 18757–18775, 2022.
[2] G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network”, Mar. 2015, arxiv.org/abs/1503.02531.
[3] L. Wang and K. -J. Yoon, "Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3048-3068,June 2022.
[4] J. Park and A. No, “Prune Your Model Before Distill It,” Jul.2022, arxiv.org/abs/2109.14960.
[5] S. Lin et al., “Filter Pruning for Efficient CNNs via Knowledge- driven Differential Filter Sampler,” Jul.2023,arxiv.org/abs/2307.00198.
[6] M. S. Rana, M. N. Nobi, B. Murali and A. H. Sung, "Deepfake Detection: A Systematic Literature Review," IEEE Access, vol. 10, pp. 25494-25513, 2022.
[7] Y. Li, M.-C. Chang, and S. Lyu, “In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking,” Jun 2018,arxiv.org/abs/1806.02877.
[8] T. Jung, S. Kim, and K. Kim, “DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern,” IEEE Access, vol. 8, pp. 83144–83154, 2020.
[9] X. Yang, Y. Li, and S. Lyu, “Exposing Deep Fakes Using Inconsistent Head Poses,” Nov.2018, arxiv.org/abs/1811.00661.
[10] T. Wang, X. Liao, K. P. Chow, X. Lin, and Y. Wang, "Deepfake detection: A comprehensive survey from the reliability perspective," ACM Computing Surveys, vol. 57, no. 3, Mar. 2025.
[11] M. Gachchannavar, J. R. Naveenkumar, and R. Velangi, "A survey an optimized dense CNN model for recognizing deepfake images," International Journal for Multidisciplinary Research, vol. 6, no. 4, Aug. 2024.
[12] M. Kim, S. Tariq, and S. S. Woo, "FReTAL: Generalizing deepfake detection using knowledge distillation and representation learning," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Nashville, TN, USA, pp. 1001–1012, 2021.
[13] M. Kim, S. Tariq, and S. S. Woo, "CoReD: Generalizing fake media detection with continual representation using distillation," in Proc. 29th ACM Int. Conf. Multimedia (MM), Chengdu, China, pp. 337–346 , Oct. 2021.
[14] C. Zhou et al., “Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection,”Feb.2023, arxiv.org/abs/2302.10437.
[15] Y. Lin, H. Chen, B. Li, and J. Wu, "Towards generalizable deepfake face forgery detection with semi-supervised learning and knowledge distillation," in Proc. IEEE Int. Conf. Image Process. (ICIP), Bordeaux, France, pp. 576–580, Oct. 2022.
[16] C. Wang, L. Meng, Z. Xia, N. Ren, and B. Ma, "Cross-domain deepfake detection based on latent domain knowledge distillation," IEEE Signal Process. Lett., vol. 32, pp. 896–900, 2025.
[17] B. M. Le and S. S. Woo, “ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low- Quality Compressed Deepfake Images,”Dec.2021, arxiv.org/abs/2112.03553.
[18] L. Al Amin, Md. I. Hossain, T. T. Nguyen, T. Jahan, M. Islam, and F. Quader, “Uncovering Critical Features Deepfake Detection through the Lottery Ticket Hypothesis,”,Jul. 2025 ,arxiv.org/abs/2507.15636.
[19] L. Chen, Y. Chen, J. Xi, and X. Le, "Knowledge from the original network: Restore a better pruned network with knowledge distillation," Complex Intell. Syst., vol. 8, no. 2, pp. 709–718, Apr. 2022.
[20] C. Deng, D. Jing, Z. Ding, and Y. Han, "Sparse channel pruning and assistant distillation for faster aerial object detection," Remote Sens., vol. 14, no. 21, p. 5347, 2022.
[21] N. Aghli and E. Ribeiro, "Combining weight pruning and knowledge distillation for CNN compression," in Proc.IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Nashville, TN, USA, pp. 3185–3192, 2021.
[22] D. Chen et al., “EPSD: Early Pruning with Self-Distillation for Efficient Model Compression,” Jan.2024, arxiv.org/abs/2402.00084.
[23] Y. Liu, K. Fan, and W. Zhou, "Iterative filter pruning with combined feature maps and knowledge distillation," Int. J. Mach. Learn. Cybern., vol. 16, no. 3, pp. 1955–1969, Mar. 2025.
[24] X. Dan, Y. Zhang, L. Li, and H. Wang, "PDD: Pruning neural networks during knowledge distillation," Cognit. Comput., vol. 16, no. 6, pp. 3457–3467, Nov. 2024.
[25] Kumar, A., Yin, B., Shaikh, A. M., et al., “CorrNet: Pearson Correlation Based Pruning for Efficient Convolutional Neural Networks,” International Journal of Machine Learning and Cybernetics, vol. 13, pp. 3773–3783, Springer, 2022.
[26] Singh, P., Verma, V., Rai, P., Namboodiri, V., “Leveraging Filter Correlations for Deep Model Compression,” Proceedings of the Conference/Journal, pp. 824–833, 2020.
[27] Xu, J., Liu, C., Qian, H., Zhu, Q., Chen, J., “Pruning of the Object Detection Model Based on Multi-level Feature De-correlation,” Proceedings of the 2nd International Conference on Artificial Intelligence of Things and Computing (AITC ’25), pp. 245–250, New York, USA, 2025.
[28] Li, G., Shao, H., Deng, X., Jiang, Y., “Adaptive Convolutional Network Pruning through Pixel-Level Cross-Correlation and Channel Independence for Enhanced Model Compression,” Engineering Applications of Artificial Intelligence, vol. 154, 2025.
[29] Wang, W., Fu, C., Guo, J., Cai, D., He, X., “COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning,” Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. 3785–3791, 2019.
[30] He, J., Chen, B., Ding, Y., Li, D., “Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration,” Computer Vision – ACCV 2020, Lecture Notes in Computer Science, vol. 12625, pp. —, Springer, 2021.
[31] M. Nadeem, R. Imam, R. Al-Refai, M. Chkir, M. Hoda, and A. El Saddik, “EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation,” Jan. 2024, arxiv.org/abs/2401.06957.
