Fake face image detection using feature network

D Jayaram; M Venu Gopalachari; S. Rakesh; J Shiva Sai; G Kiran Kumar

doi:10.53730/ijhs.v6nS5.9310

Authors

D Jayaram Assistant Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
M Venu Gopalachari
mvenugopalachari_it@cbit.ac.in
Associate Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
S. Rakesh Assistant Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
J Shiva Sai Assistant Professor, CSE Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
G Kiran Kumar Assistant Professor, CSE Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India

Keywords:

Fake face image detection, Feature Network, Social Network Data, Deep learning, Pairwise learning

Abstract

In the recent times, the image data in social networks such as Instagram, Whatsapp, Facebook, Snapchat, twitter etc has an exponential growth in terms of volume, variety due to the velocity of the data stream. On the other hand, the advancements in the image and video processing led to increase in the fake images relatively in huge volumes. Due to the involvement in spreading fake news and leading mob incitements fake images became major concern to handle that demands an efficient a fake image detector is of at most concern to entire social networking organizations. In this paper, a deep learning framework is proposed that differentiates fabricated parts of the image from the real image using supervised learning strategies. Also a modified neural network structure called the Fake Feature Network is proposed in this work which consists of advanced convolution networks. In order to make model effective, the proposed methodology has a two major steps in learning which combines a modified neural structure that uses classifier and pairwise learning for the fake image detection.

Downloads

Download data is not yet available.

References

Chih-Chung Hsu, Yi-Xiu Zuhang and Chia-Yen Lee. “Deep Fake Image Detection Based on Pairwise Learning”, Published in MDPI, Jan 3 2020

Muhammed Asfal Villain, Johns Paul, Kuncheria Kuruvilla and Eldo P Elias. “Fake Image Detection using Machine Learning” In proceedings of IEE, Mar-Arpil 2017.

Oquab, M.; Bottou, L.; Laptev, I.; Sivic, J.” Is object localization for free?-weakly-supervised learning with convolutional neural networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015.

Zhang, H.; Goodfellow, I.; Metaxas, D.; Odena, A. “Self-Attention generative adversarial networks”. In Proceedings of the 36th International Conference on Machine Learning; Chaudhuri, K., Salakhutdinov, R., Eds.; PMLR: Long Beach, CA, USA, 2019

Marra, F.; Gragnaniello, D.; Cozzolino, D.; Verdoliva, L. “Detection of GAN-Generated Fake Images over Social Networks.” In Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval, Miami, FL, USA, 10–12 April 2018.

Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; Courville, A.C. “Improved training of wasserstein gans.” In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4–9 December 2017.

Mao, X.; Li, Q.; Xie, H.; Lau, R.Y.; Wang, Z.; Smolley, S.P. “Least squares generative adversarial networks”. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017.

Miyato, T.; Kataoka, T.; Koyama, M.; Yoshida, Y.” Spectral normalization for generative adversarial networks.” In Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada, 30 April–3 May 2018.

Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.;Bernstein, M.; et al. “ImageNet large scale visual recognition challenge.” Int. J. Comput. Vis. (IJCV) 2015, 115, 211–252.

Zhengzhe Liu, Xiaojuan Qi, Jiaya Jia and P. Torr, "Global texture enhancement for fake face detection in the wild", 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8057-8066, 2020.

Xiaodan Li, Yining Lang, Yuefeng Chen, Xiaofeng Mao, Yuan He, Shuhui Wang, et al., "Sharp multiple instance learning for deepfake video detection", Proceedings of the 28th ACM International Conference on Multimedia, Oct 2020.

Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram and Cristian Canton Ferrer, "The deepfake detection challenge (DFDC) preview dataset", arXiv preprint arXiv:1910.08854 [cs.CV], 2019.

[Luca Guarnera, Oliver Giudice, Cristina Nastasi and Sebastiano Battiato, "Preliminary forensics analysis of deepfake images", arXiv preprint arXiv:2004.12626 [cs.CV], 2020.

Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

S. Azarian-Pour, M. Babaie-Zadeh and A. R. Sadri, "An automatic JPEG ghost detection approach for digital image forensics," 2016 24th Iranian Conference on Electrical Engineering (ICEE), 2016, pp. 1645-1649.

Humeau-Heurtier, "Texture feature extraction methods: A survey", IEEE Access, vol. 7, pp. 8975-9000, 2019.