Exposing deepfake using fusion of deep-learned and hand-crafted features-Reference-Cited by-同舟云学术

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Exposing deepfake using fusion of deep-learned and hand-crafted features

Published:2023-09-05 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Megahed Amr,Han Qi,Fadl Sondos

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16329-2.pdf

Reference47 articles.

1. Afchar, D, Nozick, V, Yamagishi, J, Echizen, I (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), p. 1–7. IEEE

2. Bakas J, Naskar R, Dixit R (2019) Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between haralick coded frames. Multimed Tools Appl 78(4):4905–4935. https://doi.org/10.1007/s11042-018-6570-8

3. Bayar, B, Stamm, MC(2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, p. 5–10. ACM

4. Boylan, JF(2018) The New York Times. Will DeepFake Technology Destroy Democracy? https://www.nytimes.com/2018/10/17/opinion/deep-fake-technology-democracy.html

5. Chatfield, K, Simonyan, K, Vedaldi, A, Zisserman, A (2014) Return of the devil in the details: Delving deep into convolutional nets. In: British Machine Vision Conference

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