Deep Classifiers and Wavelet Transformation for Fake Image Detection


  • Stanislaw Osowski Warsaw University of Technology, Poland
  • Maciej Golgowski Military University of Technology, Poland



continuous wavelet transform, convolutional neural networks, deep fake, ensemble of classifiers


The paper presents the computer system for detecting deep fake images in video films. The system is based on applying
continuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.


Download data is not yet available.


A. Rossler et al., "FaceForensics++: Learning to Detect Manipulated Facial Images", in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, South Korea, 2019. DOI:

L. Jaing, R. Li, W. Wu, C. Qian, and C.C. Loy, "Deeperforensics-1.0: a Large-scale Data Set for Real-world Face Forgery Detection", 2020. DOI:

P. Yu, Z. Xia, J. Fei, and Y. Lu, "A Survey on Deepfake Video Detection", IET-Biometrics, vol. 10, no. 6, pp. 607-624, 2021. DOI:

D. Cozzolino, G. Poggi, and L. Verdoliva, "Extracting Camera-based Finger Prints for Video Forensics", in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, USA, 2019.

H.H. Nguyen, J. Yamagishi, and I. Echizen, "Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos", in: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, pp. 2307-2311, 2019. DOI:

D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, "MesoNet: A Compact Facial Video Forgery Detection Network", in: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 2018. DOI:

S.H. Silva et al., "Deepfake Forensics Analysis: An Explainable Hierarchical Ensemble of Weakly Supervised Models", Forensic Science International: Synergy, vol. 4, art. no. 100217, 2022. DOI:

S.S. Shet et al., "Deepfake Detection in Digital Media Forensics", Global Transitions Proceedings, vol. 3, no. 1, pp. 74-79, 2022.

U.A. Ciftci, I. Demir, and L. Yin, "FakeCatcher: Detection of Synthetic Portrait Videos Using Biological Signals", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 10, 2020. DOI:

E. Sabir et al., "Recurrent Convolutional Strategies for Face Manipulation Detection in Videos", arXiv:1905.00582v3, 2019.

FaceForensics. Database of FaceForensics++ [Online]. Available:

M. Massod et al., "Deepfakes Generation and Detection: State-of-the-art, Open Challenges, Countermeasures, and Way Forward", Applied Intelligence, vol. 53, pp. 3974-4026, 2022. DOI:

N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection", in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, USA, 2005.

J.J.V. Hernandez, J.I. de la Rosa, G. Rodriguez, and J.L. Flores, "The 2nd Continuous Wavelet Transform: Applications in Fringe Pattern Processing for Optical Measurement Techniques", in: Wavelet Theory and Its Applications, IntechOpen, pp.173-193, 2018. DOI:

J. Brownlee, Deep Learning for Natural Language Processing. Develop Deep Learning Models for Your Natural Language Problems, Johns Hopkins University Press, Ebook, 372 p., 2018 (ISBN: 9781838550295).

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Massachusetts, 2016 (ISBN: 9780262035613).

A. Krishevsky, I. Sutskever, and G.E. Hinton "ImageNet Classification with Deep Convolutional Neural Networks", Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017. DOI:

K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition", 2015. DOI:

A.G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", 2017.

G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, "Densely Connected Convolutional Networks", 2018. DOI:

F.N. Iandola et al., "SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <0.5MB Model Size", 2017.

X. Zhang, X. Zhou, M. Lin, and J. Sun, "ShuffleNet: an Extremely Efficient Convolutional Neural Network for Mobile Devices", 2017. DOI:

Y. Zhao et al., "Capturing the Persistence of Facial Expression Features for Deep Fake Video Detection", in: International Conference on Information and Communications Security, Beijing, China, pp. 630-645, 2019. DOI:




How to Cite

Osowski, S., & Golgowski, M. (2023). Deep Classifiers and Wavelet Transformation for Fake Image Detection. Journal of Telecommunications and Information Technology, 4(4), 1–8.




Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.