Deep Classifiers and Wavelet Transformation for Fake Image Detection
DOI:
https://doi.org/10.26636/jtit.2023.4.1336Keywords:
continuous wavelet transform, convolutional neural networks, deep fake, ensemble of classifiersAbstract
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.
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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: https://doi.org/10.1109/ICCV.2019.00009
View in Google Scholar
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: https://doi.org/10.1109/CVPR42600.2020.00296
View in Google Scholar
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: https://doi.org/10.1049/bme2.12031
View in Google Scholar
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.
View in Google Scholar
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: https://doi.org/10.1109/ICASSP.2019.8682602
View in Google Scholar
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: https://doi.org/10.1109/WIFS.2018.8630761
View in Google Scholar
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: https://doi.org/10.1016/j.fsisyn.2022.100217
View in Google Scholar
S.S. Shet et al., "Deepfake Detection in Digital Media Forensics", Global Transitions Proceedings, vol. 3, no. 1, pp. 74-79, 2022.
View in Google Scholar
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: https://doi.org/10.1109/TPAMI.2020.3009287
View in Google Scholar
E. Sabir et al., "Recurrent Convolutional Strategies for Face Manipulation Detection in Videos", arXiv:1905.00582v3, 2019.
View in Google Scholar
FaceForensics. Database of FaceForensics++ [Online]. Available: https://github.com/ondyari/FaceForensics
View in Google Scholar
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: https://doi.org/10.1007/s10489-022-03766-z
View in Google Scholar
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.
View in Google Scholar
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: https://doi.org/10.5772/intechopen.74813
View in Google Scholar
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).
View in Google Scholar
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Massachusetts, 2016 (ISBN: 9780262035613).
View in Google Scholar
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: https://doi.org/10.1145/3065386
View in Google Scholar
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition", 2015. DOI: https://doi.org/10.1109/CVPR.2016.90
View in Google Scholar
A.G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", 2017.
View in Google Scholar
G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, "Densely Connected Convolutional Networks", 2018. DOI: https://doi.org/10.1109/CVPR.2017.243
View in Google Scholar
F.N. Iandola et al., "SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <0.5MB Model Size", 2017.
View in Google Scholar
X. Zhang, X. Zhou, M. Lin, and J. Sun, "ShuffleNet: an Extremely Efficient Convolutional Neural Network for Mobile Devices", 2017. DOI: https://doi.org/10.1109/CVPR.2018.00716
View in Google Scholar
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: https://doi.org/10.1007/978-3-030-41579-2_37
View in Google Scholar
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