ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning

Ruohan Zong; Yang Zhang; Lanyu Shang; Dong Wang

doi:10.1145/3543507.3583869

ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning

Ruohan Zong
, Yang Zhang
, Lanyu Shang
, Dong Wang

University of Illinois Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
Publisher	Association for Computing Machinery, Inc
Pages	3994-4003
Number of pages	10
ISBN (Electronic)	9781450394161
DOIs	https://doi.org/10.1145/3543507.3583869
State	Published - Apr 30 2023
Externally published	Yes
Event	32nd ACM World Wide Web Conference, WWW 2023 - Austin, United States Duration: Apr 30 2023 → May 4 2023

Publication series

Name	ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Conference

Conference	32nd ACM World Wide Web Conference, WWW 2023
Country/Territory	United States
City	Austin
Period	4/30/23 → 5/4/23

ASJC Scopus Subject Areas

Computer Networks and Communications
Software

Keywords

Contrastive Learning
Fauxtography Detection
Semi-supervised Learning
Web Misinformation

Access to Document

10.1145/3543507.3583869

Cite this

Zong, R., Zhang, Y., Shang, L., & Wang, D. (2023). ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 (pp. 3994-4003). (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023). Association for Computing Machinery, Inc. https://doi.org/10.1145/3543507.3583869

ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. / Zong, Ruohan; Zhang, Yang; Shang, Lanyu et al.
ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, Inc, 2023. p. 3994-4003 (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zong, R, Zhang, Y, Shang, L & Wang, D 2023, ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. in ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, Association for Computing Machinery, Inc, pp. 3994-4003, 32nd ACM World Wide Web Conference, WWW 2023, Austin, United States, 4/30/23. https://doi.org/10.1145/3543507.3583869

Zong R, Zhang Y, Shang L, Wang D. ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, Inc. 2023. p. 3994-4003. (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023). doi: 10.1145/3543507.3583869

Zong, Ruohan ; Zhang, Yang ; Shang, Lanyu et al. / ContrastFaux : Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, Inc, 2023. pp. 3994-4003 (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023).

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title = "ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning",

keywords = "Contrastive Learning, Fauxtography Detection, Semi-supervised Learning, Web Misinformation",

author = "Ruohan Zong and Yang Zhang and Lanyu Shang and Dong Wang",

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ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this