On the Correlation Between Deepfake Detection Performance and Image Quality Metrics-Reference-Cited by-同舟云学术

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On the Correlation Between Deepfake Detection Performance and Image Quality Metrics

Published:2024-07 Issue: Volume: Page:14-19
ISSN:
Container-title:Proceedings of the 3rd ACM Workshop on the Security Implications of Deepfakes and Cheapfakes
language:
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Author:

Kim Hyunjoon¹^ORCID,Lee Jaehee¹^ORCID,Park Leo Hyun¹^ORCID,Kwon Taekyoung¹^ORCID

Affiliation:

1. Graduate School of Information / Information Security & AI Security Lab, Yonsei University, Republic of Korea

Funder

Institute of Information communications Technology Planning Evaluation (IITP)

National Research Foundation of Korea (NRF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660354.3660358

Reference23 articles.

1. MesoNet: a Compact Facial Video Forgery Detection Network

2. SimSwap

3. Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. 2020. The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397 (2020).

4. Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer. 2019. The deepfake detection challenge (dfdc) preview dataset. arXiv preprint arXiv:1910.08854 (2019).

5. ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

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