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Cited by 36Advanced Steganography and Watermarking TechniquesDigital Media Forensic DetectionAdversarial Robustness in Machine Learning

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RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-key Identification

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Ci, Hai, Yang, Pei, Song, Yiren, Shou, Mike Zheng·2025·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-73390-1_20
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  • Follow-on work such as RingID revisits the mechanism and analyzes robustness sources.

    “Follow-on work such as RingID revisits the mechanism and analyzes robustness sources”

    From A Review of Tools and Technologies to Combat Deepfakes (2026) · cites this paper as [33]

  • Watermarking is an arms race rather than a one-time fix.

    “security research also investigates watermark removal and attack surfaces, underscoring that watermarking is an arms race rather than a one-time fix [33].”

    From A Review of Tools and Technologies to Combat Deepfakes (2026) · cites this paper as [33]

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References 33 · Cited by 36

Research works printed but not linked: 46

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Metadata

Authors
Ci, H., Yang, P., Song, Y., & Shou, M. Z.
Affiliations
National University of Singapore (SG)
Corresponding author
Shou, Mike Zheng
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2024-10-31
Pages
338-354
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-73390-1_20
OpenAlex
W4403922501
Cited by
36
References
33
Citations by year
2026: 23 · 2025: 11 · 2024: 2
FWCI
7.46
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Funders
Ministry of Education - Singapore (FY2022, Tier 1)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
distribution shift, watermark robustness, watermark attacks

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