Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review

Authors

  • Mohd Akbar Integral University, Lucknow, India
  • Mohd Suaib Integral University, Lucknow, India
  • Mohd Shahid Hussain University of Technology and Applied Sciences-CAS Ibri, Oman

DOI:

https://doi.org/10.24203/ijcit.v11i2.282

Keywords:

Deep learning; Encoder; Decoder Generative adversarial networks (GANs). Question Generation, NLP, Intelligent Tutoring System (ITS)

Abstract

Deep-Fake Technique is a new scientific method that uses Artificial-Intelligince to make fake videos with an affect of facial expressions and coordinated movement of lips. This technology is frequently employed in a variety of contexts with various goals. Deep-Fake technology is being used to generate an extremely realistic fake video that can be widely distributed to promote false information or fake news about any celebrity or leader that was not created by them. Because of the widespread use of social media, these fraudulent videos can garner billions of views in under an hour and have a significant impact on our culture. Deep-Fakes are a threat to our celebrities, democracy, religious views, and commerce, according to the findings, but they can be managed through rules and regulations, strong company policy, and general internet user awareness and education. We need to devise a process for examining such video and distinguishing between actual and fraudulent footage.

References

Jafar, M.T., et al. Forensics and analysis of Deep_fake videos. in 2020 11th international conference on information and communication systems (ICICS). 2020. IEEE.

Castillo Camacho, I. and K. Wang, A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics. J Imaging, 2021. 7(4).

Nguyen, T.T., et al., Deep learning for Deep_fake s creation and detection: A survey. arXiv preprint arXiv:1909.11573, 2019.

Westerlund, M., The emergence of Deep_fake technology: A review. Technology Innovation Management Review, 2019. 9(11).

Patel, M., et al. Trans-DF: a transfer learning-based end-to-end Deep_fake detector. in 2020 IEEE 5th international conference on computing communication and automation (ICCCA). 2020. IEEE.

Letzing, J., How to tell reality from a Deep_fake . https://www.weforum.org/agenda/2021/04/are-we-at-a-tipping-point-on-the-use-of-Deep_fake s/, 2021.

Swathi, P. and S. Sk. Deep_fake Creation and Detection: A Survey. in 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). 2021. IEEE.

Badrinarayanan, V., A. Kendall, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 2017. 39(12): p. 2481-2495.

Yang, W., et al., FV-GAN: Finger vein representation using generative adversarial networks. IEEE Transactions on Information Forensics and Security, 2019. 14(9): p. 2512-2524.

Tewari, A., et al., High-fidelity monocular face reconstruction based on an unsupervised model-based face autoencoder. IEEE transactions on pattern analysis and machine intelligence, 2018. 42(2): p. 357-370.

Guo, Y., et al., Fuzzy sparse autoencoder framework for single image per person face recognition. IEEE transactions on cybernetics, 2017. 48(8): p. 2402-2415.

Liu, F., L. Jiao, and X. Tang, Task-oriented GAN for PolSAR image classification and clustering. IEEE transactions on neural networks and learning systems, 2019. 30(9): p. 2707-2719.

Cao, J., et al., 3D aided duet GANs for multi-view face image synthesis. IEEE Transactions on Information Forensics and Security, 2019. 14(8): p. 2028-2042.

Zhang, W., C. Zhao, and Y. Li, A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis. Entropy (Basel), 2020. 22(2).

Güera, D. and E.J. Delp. Deep_fake video detection using recurrent neural networks. in 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). 2018. IEEE.

16. Chesney, B. and D. Citron, Deep fakes: A looming challenge for privacy, democracy, and national security. Calif. L. Rev., 2019. 107: p. 1753.

17. Botha, J. and H. Pieterse. Fake news and Deep_fake s: A dangerous threat for 21st century information security. in ICCWS 2020 15th International Conference on Cyber Warfare and Security. Academic Conferences and publishing limited. 2020.

18. Hofesmann, E., The State of Deep_fake s in 2020. https://www.skynettoday.com/overviews/state-of-Deep_fake s-2020, 2020.

19. NCRB Report 2020. https://ncrb.gov.in/sites/default/files/CII%202020%20Volume%201.pdf, 2021.

20. Samuel, S., A guy made a Deep_fake app to turn photos of women into nudes. It didn’t go well. 2019.

21. Vurimi Veera Venkata Naga Sai Vamsi, S.S.S., Sodum Sai Mohan Reddy, Sharon S Rose, Sona R Shetty, S Sathvika, Supriya M S, Sahana P Shankar, Deep_fake Detection in Digital Media Forensics. Global Transitions Proceedings, 2022.

Kanozia, R., et al., A study on fake news subject matter, presentation elements, tools of detection, and social media platforms in India. Asian Journal for Public Opinion Research, 2021. 9(1): p. 48-82.

Lyu, S. Deep_fake detection: Current challenges and next steps. in 2020 IEEE international conference on multimedia & expo workshops (ICMEW). 2020. IEEE.

Guarnera, L., et al. Preliminary forensics analysis of Deep_fake images. in 2020 AEIT International Annual Conference (AEIT). 2020. IEEE.

Trinh, L., et al. Interpretable and trustworthy Deep_fake detection via dynamic prototypes. in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021.

Younus, M.A. and T.M. Hasan. Effective and fast Deep_fake detection method based on haar wavelet transform. in 2020 International Conference on Computer Science and Software Engineering (CSASE). 2020. IEEE.

Koopman, M., A.M. Rodriguez, and Z. Geradts. Detection of Deep_fake video manipulation. in The 20th Irish machine vision and image processing conference (IMVIP). 2018.

Lukas, J., J. Fridrich, and M. Goljan, Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security, 2006. 1(2): p. 205-214.

Rosenfeld, K. and H.T. Sencar. A study of the robustness of PRNU-based camera identification. in Media Forensics and Security. 2009. International Society for Optics and Photonics.

Downloads

Published

2022-06-21

How to Cite

Akbar, M. ., Suaib, M., & Hussain, M. S. . (2022). Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review. International Journal of Computer and Information Technology(2279-0764), 11(2). https://doi.org/10.24203/ijcit.v11i2.282

Issue

Section

Articles