Open Access Open Access  Restricted Access Subscription Access

Processing of Deepfake Images Using Deep Learning

Arushi Kataria, Atrakesh Pandey

Abstract


Face identification has been a significant exploration subject in the mid 2000s. Right around twenty years after the fact, this issue is essentially settled and face recognition is accessible as a library in most programming dialects. Indeed, even face-trade innovation is the same old thing and has been around for a couple of years. Such an advancement depends on neural organizations whole computational models that are inexactly roused by the manner in which genuine cerebrums measure data. This tale procedure permits producing alleged deepfakes, which really transform an individual's face to imitate another person's highlights, in spite of the fact that safeguarding the first outward appearance. At the point when utilized appropriately, this method permits formation of photorealistic recordings at an unfathomably ease.


Full Text:

PDF

References


Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2019 (pp. 4401–4410).

Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A, Ortega-Garcia J. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion. 2020 Dec 1; 64: 131–48.

Chollet F. Xception: Deep learning with depth wise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 1251–1258).

Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 8789–8797).

Alan Zucconi, 2018. Understanding the Technology Behind Deepfakes [Online]. Available from https://www.alanzucconi.com/2018/03/14/understanding-the technology-behind-deepfakes/

Mirsky Y, Lee W. The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR). 2021 Jan 2; 54(1): 1–41.

Almars AM. Deepfakes detection techniques using deep learning: a survey. Journal of Computer and Communications. 2021 May 12; 9(5): 20–35.

Shahzad HF, Rustam F, Flores ES, Luís Vidal Mazón J, de la Torre Diez I, Ashraf I. A Review of Image Processing Techniques for Deepfakes. Sensors. 2022 Jan; 22(12): 4556.

Mirsky Y, Lee W. The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR). 2021 Jan 2; 54(1): 1–41.

Agarwal S, Farid H, Fried O, Agrawala M. Detecting deep-fake videos from phoneme-viseme mismatches. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops 2020 (pp. 660–661).




DOI: https://doi.org/10.37591/jocta.v13i2.931

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Journal of Computer Technology & Applications