A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects
Abstract & Details
Research Area
Computer Science & Engineering
Keywords
Deep learning
DeepFake
CNNs
GANs
image classification
image forensics
Abstract
Deep learning is being used so frequently now that media synthesis and manipulation have never been more realistic. The go-to tool for controlling the media is now Deepfake. Despite having uses in the entertainment industry, this technology is also susceptible to political manipulation and other issues. This technology has made significant advancements and supports a variety of applications in TV channels, the video game industry, and the film industry, such as improving visual effects in movies, as well as a number of illegal actions, like spreading false information by imitating well-known individuals. Research on DeepFake identification utilizing deep neural networks (DNNs) has drawn more attention in order to recognize and categorize DeepFakes. DeepFake is essentially regenerated material that has had some information added to it or replaced by using the DNN model.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | SHRUTHI S | RR Institute of Technology |
| 2 | Manjunath R | R RInstitute of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
S, SHRUTHI & R, Manjunath (2023). A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects. International Journal of Advance Research and Innovative Ideas In Education, 9(4), 3049-3054.
MLA Style
S, SHRUTHI, and Manjunath R. "A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 4, 2023, pp. 3049-3054.
IEEE Style
SHRUTHI S and Manjunath R, "A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 4, pp. 3049-3054, 2023.
Vancouver Style
S SHRUTHI, R Manjunath. A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(4):3049-3054.
Harvard Style
S, SHRUTHI & R, Manjunath (2023) 'A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects', International Journal of Advance Research and Innovative Ideas In Education, 9(4), pp. 3049-3054.
Chicago Style
S, SHRUTHI and Manjunath R. "A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects." International Journal of Advance Research and Innovative Ideas In Education 9, no. 4 (2023): 3049-3054.
Turabian Style
S, SHRUTHI and Manjunath R. "A Review on Deepfake Multimedia Data on Heterogeneous Filter Effects." International Journal of Advance Research and Innovative Ideas In Education 9, no. 4 (2023): 3049-3054.
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