Detection Enhancement for Various Deepfake Types Based on Residual Noise and Manipulation Traces-Reference-Cited by-同舟云学术

Detection Enhancement for Various Deepfake Types Based on Residual Noise and Manipulation Traces

Author:

Kang Jihyeon¹,Ji Sang-Keun²^ORCID,Lee Sangyeong³^ORCID,Jang Daehee⁴,Hou Jong-Uk³^ORCID

Affiliation:

1. Graduate School of Information Security, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

2. School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

3. School of Software, Hallym University, Chuncheon, Republic of Korea

4. Department of Security Engineering, Sungshin Women’s University, Seoul, Republic of Korea

Funder

National Research Foundation of Korea

Korean Government

Hallym University Research Fund 2021

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference64 articles.

2. OpenFace: A general-purpose face recognition library with mobile applications;amos,2016

4. Detection of deepfake video manipulation;koopman;Proc 20th Irish Mach Vis image Process Conf (IMVIP),2018

5. Identification of deep network generated images using disparities in color components;li;arXiv 1808 07276,2018

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