Countering Malicious DeepFakes: Survey, Battleground, and Horizon-Reference-Cited by-同舟云学术

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

Published:2022-05-04 Issue:7 Volume:130 Page:1678-1734
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Juefei-Xu Felix^ORCID,Wang Run^ORCID,Huang Yihao^ORCID,Guo Qing^ORCID,Ma Lei^ORCID,Liu Yang^ORCID

Funder

National Research Foundation Singapore

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s11263-022-01606-8.pdf

Reference326 articles.

1. 115th Congress. (2018). S.3805—Malicious Deep Fake Prohibition Act of 2018. https://www.congress.gov/bill/115th-congress/senate-bill/3805

2. 116th Congress. (2019a). H.R.3230—Defending Each and Every Person from False Appearances by Keeping Exploitation Subject to Accountability Act of 2019. https://www.congress.gov/bill/116th-congress/house-bill/3230/

3. 116th Congress. (2019b). S.2065—Deepfake Report Act of 2019. https://www.congress.gov/bill/116th-congress/senate-bill/2065

4. Abiantun, R., Juefei-Xu, F., Prabhu, U., & Savvides, M. (2019). SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions. Pattern Recognition, 90, 308–324.

5. Adobe. (2021a). Adobe audition. Retrieved August 1, 2021, from https://www.adobe.com/products/audition.html (online).

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