A Detection of Deep Fake in Face Images Using Deep Learning

A Detection of Deep Fake in Face Images Using Deep Learning


  • Hanady Sabah Al Nahrain




Deep learning, Deep fake, Generative Adversarial Network, Convolution Neural Network, Principal Component Analysis.


Fake images are one of the most widespread phenomena that have a significant influence on our social life, particularly in the world of politics and celeb. Nowadays, generating fake images has become very easy due to the powerful yet simple applications in mobile devices that navigate in the social media world and with the emergence of the Generative Adversarial Network (GAN) that produces images which are indistinguishable to the human eye. Which makes fake images and fake videos easy to perform, difficult to detect, and fast to spread. As a result, image processing and artificial intelligence play an important role in solving such issues. Thus, detecting fake images is a critical problem that must be controlled and to prevent these numerous harmful effects. This research proposed utilizing the most popular algorithm in deep learning is (Convolution Neural Network) to detect the fake images.

The first steps includes a preprocessing which start with converting images from RGB to YCbCr color space, after that entering the Gamma correction. finally extract edge detection by entering the Canny filter on them. After that, utilizing two different method of detection by applying (Convolution Neural Network with Principal Component Analysis) and (Convolution Neural Network without Principal Component Analysis) as a classifiers.

The results reveal that the use of CNN with PCA in this research results in acceptable accuracy. In contrast, using CNN only gave the highest level of accuracy in detecting manipulated images.


Guarnera, Luca, Oliver Giudice, and Sebastiano Battiato. "Deepfake detection by analyzing convolutional traces." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 666-667. 2020.

Wang, L. S. "On the integrated regulation of “deep forgery”." intelligent technology, Oriental Law, 2019.

Chang, Xu, Jian Wu, Tongfeng Yang, and Guorui Feng. "Deepfake face image detection based on improved VGG convolutional neural network." In 2020 39th chinese control conference (CCC), pp. 7252-7256. IEEE, 2020.

Abadi, Martín, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin et al. "{TensorFlow}: a system for {Large-Scale} machine learning." In 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp. 265-283. 2016.

Chollet, François. "keras. GitHub repository." https://github. com/fchollet/keras>. Accessed on 25 (2015): 2017.

Tewari, Ayush, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. "Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction." In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1274-1283. 2017.

Antipov, Grigory, Moez Baccouche, and Jean-Luc Dugelay. "Face aging with conditional generative adversarial networks." In 2017 IEEE international conference on image processing (ICIP), pp. 2089-2093. IEEE, 2017.

Guarnera, Luca, Oliver Giudice, Cristina Nastasi, and Sebastiano Battiato. "Preliminary forensics analysis of deepfake images." In 2020 AEIT international annual conference (AEIT), pp. 1-6. IEEE, 2020.

Wang, Yonghui, Vahid Zarghami, and Suxia Cui. "Fake Face Detection using Local Binary Pattern and Ensemble Modeling." In 2021 IEEE International Conference on Image Processing (ICIP), pp. 3917-3921. IEEE, 2021.

Taeb, Maryam, and Hongmei Chi. "Comparison of Deepfake Detection Techniques through Deep Learning." Journal of Cybersecurity and Privacy 2, no. 1, 2022.

Tariq, Shahroz, Sangyup Lee, Hoyoung Kim, Youjin Shin, and Simon S. Woo. "Detecting both machine and human created fake face images in the wild." In Proceedings of the 2nd international workshop on multimedia privacy and security, pp. 81-87. 2018.

Chang, Xu, Jian Wu, Tongfeng Yang, and Guorui Feng. "Deepfake face image detection based on improved VGG convolutional neural network." In 2020 39th chinese control conference (CCC), pp. 7252-7256. IEEE, 2020.

xhlulu. 140k Real and Fake Faces [Dataset]. https://www.kaggle.com/xhlulu/140k-real-and-fake-faces 2020.

Taloba, Ahmed I., Dalia A. Eisa, and Safaa SI Ismail. "A comparative study on using principle component analysis with different text classifiers." arXiv preprint arXiv:1807.03283, 2018.

Karamizadeh, Sasan, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, and Alireza Hooman. "An overview of principal component analysis." Journal of Signal and Information Processing 4, 2020.

Ahmad, Muhammad, Adil Mehmood Khan, Joseph Alexander Brown, Stanislav Protasov, and Asad Masood Khattak. "Gait fingerprinting-based user identification on smartphones." In 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3060-3067. IEEE, 2016.

Ahmad, Muhammad, Dr Ihsan Ul Haq, Qaisar Mushtaq, and Muhammad Sohaib. "A new statistical approach for band clustering and band selection using K-means clustering." Int. J. Eng. Technol 3, no. 6, 2011

Arjun, V. MANE, Ramesh R. MANZA, and V. KALE Karbhari. "Human face recognition using superior principal component analysis (SPCA)." International Journal of Computer Theory and Engineering 2, no. 5, 2010

Traore, Boukaye Boubacar, Bernard Kamsu-Foguem, and Fana Tangara. "Deep convolution neural network for image recognition." Ecological Informatics 48, 2018

Aloysius, Neena, and M. Geetha. "A review on deep convolutional neural networks." In 2017 international conference on communication and signal processing (ICCSP), pp. 0588-0592. IEEE, 2017.

Ali, Jehad, Rehanullah Khan, Nasir Ahmad, and Imran Maqsood. "Random forests and decision trees." International Journal of Computer Science Issues (IJCSI) 9, no. 5, 2012.

SHAHANE, PRIYANKA, and DEIPALI GORE. "Detection of Fake Profiles on Twitter using Random Forest & Deep Convolutional Neural Network, International Journal of Management, Technology And Engineering, 2019.

Allcott, Hunt, and Matthew Gentzkow. "Social media and fake news in the 2016 election." Journal of economic perspectives 31, no. 2, 2017.

Gilda, Shlok. "Notice of violation of IEEE publication principles: Evaluating machine learning algorithms for fake news detection." In 2017 IEEE 15th student conference on research and development (SCOReD), pp. 110-115. IEEE, 2017.

Han, J., Kamber, M. and Pei, J. “Data mining concepts and techniques third edition,” The Morgan Kaufmann Series in Data Management Systems, 5(4), pp. 83–124 . 2011.

Shu, Kai, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. "Fake news detection on social media: A data mining perspective." ACM SIGKDD explorations newsletter 19, no. 1, 2017.




How to Cite

Sabah, H. (2022). A Detection of Deep Fake in Face Images Using Deep Learning. Wasit Journal of Computer and Mathematics Sciences, 1(4), 94–111. https://doi.org/10.31185/wjcm.92