Enhancing Cheapfake Detection: An Approach Using Prompt Engineering and Interleaved Text-Image Model-Reference-Cited by-同舟云学术

Enhancing Cheapfake Detection: An Approach Using Prompt Engineering and Interleaved Text-Image Model

Published:2024-05-30 Issue: Volume:35 Page:1306-1311
ISSN:
Container-title:Proceedings of the 2024 International Conference on Multimedia Retrieval
language:
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Author:

Vu Dang¹^ORCID,Nguyen Minh-Nhat¹^ORCID,Nguyen Quoc-Trung¹^ORCID

Affiliation:

1. University of Science & Vietnam National University, Ho Chi Minh City, Vietnam

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652583.3657600

Reference19 articles.

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2. Flamingo: a visual language model for few-shot learning;Alayrac Jean-Baptiste;Advances in Neural Information Processing Systems,2022

3. Shivangi Aneja, Chris Bregler, and Matthias Nießner. 2021a. Cosmos: Catching out-of-context misinformation with self-supervised learning. arXiv preprint arXiv:2101.06278 (2021).

4. Shivangi Aneja, Cise Midoglu, Duc-Tien Dang-Nguyen, Sohail Ahmed Khan, Michael Riegler, Pål Halvorsen, Chris Bregler, and Balu Adsumilli. 2022. Acm multimedia grand challenge on detecting cheapfakes. arXiv preprint arXiv:2207.14534 (2022).

5. Shivangi Aneja, Cise Midoglu, Duc-Tien Dang-Nguyen, Michael Alexander Riegler, Paal Halvorsen, Matthias Nießner, Balu Adsumilli, and Chris Bregler. 2021b. MMSys' 21 grand challenge on detecting cheapfakes. arXiv preprint arXiv:2107.05297 (2021).

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1. Overview of the Grand Challenge on Detecting Cheapfakes at ACM ICMR 2024;Proceedings of the 2024 International Conference on Multimedia Retrieval;2024-05-30