Deep fake detection system based on improvement graph convolutional network
*Basim Najim Al-din AbedCorresponding authorbasim007@yahoo.comDepartment of Computer ScienceFaculty of Mathematical Sciences & Computer ScienceUniversity of TabrizTabriz, East Azerbaijan Province, 51666 15813, Iran0000-0003-0730-2979View full profile → , J. Karimpourkarimpour@tabrizu.ac.irDepartment of Computer ScienceFaculty of Mathematical Sciences & Computer ScienceUniversity of TabrizTabriz, East Azerbaijan Province, 51666 15813, Iran0000-0001-6193-3240View full profile → , Farnaz MahanMahan@tabrizu.ac.irDepartment of Computer ScienceFaculty of Mathematical Sciences & Computer ScienceUniversity of TabrizTabriz, East Azerbaijan Province, 51666 15813, Iran0000-0002-9215-6015View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 07 Jan 2025
- Published Online:
- 07 Nov 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2038
- Pages:
- 287–298
Abstract
Keywords
Subject Classifications
References
[1] I. Goodfellow, J. Shlens, and C. Szegedy, “Adversarial attacks and defenses in deepfake detection,” J. AI Ethics (2021).
[2] X. Zhang, L. Chen, Y. Li, and M. Zhao, “CNN-based architectures for high-resolution deepfake detection,” IEEE Trans. Multimedia (2022).
[3] H. Li and Y. Wang, “Adversarial robustness in GAN-based media manipulations,” Comput. Vis. Image Understand. (2023).
[4] S. Tambe, A. Pawar, and S. K. Yadav, “Deep fake videos identification using ANN and LSTM,” J. Discrete Math. Sci. Cryptogr., vol. 24, no. 8, pp. 2353–2364 (2021).
[5] T. N. Kipf, M. Welling, J. Bojchevski, and P. Liò, “Applications of graph learning in structured data analysis,” in Proc. Neural Inf. Process. Syst. (NeurIPS) (2021).
[6] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “Graph neural networks for biochemical graphs,” Nat. Mach. Intell. (2022).
[7] Y. Xu, H. Zhang, L. Wang, and J. Feng, “Graph-based representations in computer vision: Challenges and advances,” in Proc. Comput. Vis. Pattern Recognit. (CVPR) (2024).
[8] J. Sun, L. Huang, Z. He, and Q. Zhao, “Graph-based learning for resilient adversarial defense,” Pattern Recognit. Lett. (2023).
[9] J. Ma, T. Lin, X. Wang, and F. Yu, “Diffusion filters in adversarial learning,” Int. J. Comput. Vis. (2023).
[10] B. Dolhansky, R. Howes, B. Pflaum, N. Baram, and C. C. Ferrer, “The Deepfake Detection Challenge (DFDC) dataset,” arXiv preprint arXiv:2006.07397 (2020).
[11] Z. Liu, P. Luo, X. Wang, and X. Tang, “CelebA: Advancements in facial attribute detection,” IEEE Trans. Image Process. (2021).
[12] A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: A dataset for deepfake detection,” in Proc. Int. Conf. Comput. Vis. (ICCV) (2021).
[13] A. Krizhevsky, V. Nair, and G. Hinton, “CIFAR-10 dataset updates for enhanced machine learning benchmarks,” J. Mach. Learn. Res. (2024).
[14] N. Papernot, P. McDaniel, I. Goodfellow, and R. Shokri, “Adversarial training techniques in GAN-driven deepfake detection,” ACM Trans. Multimedia Comput. (2021).
[15] W. L. Hamilton, R. Ying, and J. Leskovec, “Graph neural networks for structured image analysis,” IEEE Trans. Neural Netw. Learn. Syst. (2022).
[16] X. Tang, Y. Du, J. Liu, and Z. Zhang, “Hybrid filtering for adversarial noise in GANs,” Pattern Recognit. Appl. (2023).
[17] P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. Int. Conf. Learn. Represent. (ICLR) (2020).
[18] F. Zhao, Q. Li, H. Wu, and Y. Zhang, “Attention mechanisms in graph-based deepfake detection,” IEEE Trans. Image Process. (2022).
[19] R. Gupta, M. Bansal, S. Verma, and T. Singh, “Explaining GNN decisions in deepfake detection,” J. Artif. Intell. Res. (2023).
[20] L. Chen, M. Zhou, J. Yang, and K. Wang, “Interpretable GNN layers for robust media classification,” in Proc. Neural Inf. Process. Syst. (NeurIPS) (2024).




