Earticle

초록

영어

In the following years, technology has progressed in so many ways that it has provided the cyber society with a resource that only computers can excel at, such as the art of counterfeit of media, which was before unavailable. Deepfakes are a term used to describe this kind of deception. The majority of well-documented Deep Fakes are produced using Generative Adversarial Network (GAN) Models, which are essentially two distinct Machine Learning Models that perform the roles of attack and defence. These models create and identify deepfakes until they reach a point where the morphing no longer detects the deepfakes anymore. Using this algorithm/model, it is possible to discover and create new media that has a similar demographic to the training set, resulting in the development of the ideal Deep Fake media. Because the alterations are carried out utilising advanced characteristics, they cannot be seen with the human eye. However, it is completely feasible to develop an algorithm that can automatically identify this kind of tampering carried out via the internet. This not only enables us to broaden the scope of our search beyond a single media item, but also beyond a large library of mixed media. The more it learns, the better it becomes as artificial intelligence takes over in full force with automation. In order to create better deep fakes, new models are being developed all the time, making it more difficult to distinguish between genuine and morphing material.

목차

ABSTRACT
1. Introduction
2. Background and Related Works
3. Materials and Methods
3.1 State of Art
3.2 Deepfake Generation
3.3 Deepfake Detection
4. Results And Discussions
5. Conclusion
Conflict of Interest
Acknowledgments
References

저자

• Debnath Bhattacharyya [ Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur-522502, India. ] Corresponding Author
• Eali Stephen Neal Joshua [ Department of Computer Science and Multimedia, Lincoln University College, Kuala Lumpur 47301, Malaysia ]
• N. Thirupathi Rao [ Department of Computer Science & Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam, AP, India. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793