REVIEW 2 cited by
RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We revisit Tree-Ring Watermarking, a recent diffusion model watermarking method that demonstrates great robustness to various attacks. We conduct an in-depth study on it and reveal that the distribution shift unintentionally introduced by the watermarking process, apart from watermark pattern matching, contributes to its exceptional robustness. Our investigation further exposes inherent flaws in its original design, particularly in its ability to identify multiple distinct keys, where distribution shift offers no assistance. Based on these findings and analysis, we present RingID for enhanced multi-key identification. It consists of a novel multi-channel heterogeneous watermarking approach designed to seamlessly amalgamate distinctive advantages from diverse watermarks. Coupled with a series of suggested enhancements, RingID exhibits substantial advancements in multi-key identification. Github Page: https://github.com/showlab/RingID
Forward citations
Cited by 2 Pith papers
-
SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution
A training-free watermarking framework that embeds watermarks into diffusion super-resolution noise and extracts them via DDIM inversion, reaching 99.46% bit accuracy under standard distortions and 89.29% under adapti...
-
SoK: Watermarking for AI-Generated Content
A systematization of knowledge on watermarking for AI-generated content, unifying definitions, threat models, evaluation methods, and representative schemes across modalities.
Discussion (0). Continue with ORCID to comment.