CSF: Black-box Fingerprinting via Compositional Semantics for Text-to-Image Models
arXiv SecurityArchived Apr 21, 2026✓ Full text saved
arXiv:2604.16363v1 Announce Type: new Abstract: Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violations can be detected. Existing methods require pre-deployment watermarking or internal model access, which are unavailable in commercial API deployments. We present Compositional Semantic Fingerprinting (CSF), the first black-box method for attributing fine-tuned text-to-image models to protected line
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Computer Science > Cryptography and Security
[Submitted on 20 Mar 2026]
CSF: Black-box Fingerprinting via Compositional Semantics for Text-to-Image Models
Junhoo Lee, Mijin Koo, Nojun Kwak
Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violations can be detected. Existing methods require pre-deployment watermarking or internal model access, which are unavailable in commercial API deployments. We present Compositional Semantic Fingerprinting (CSF), the first black-box method for attributing fine-tuned text-to-image models to protected lineages using only query access. CSF treats models as semantic category generators and probes them with compositional underspecified prompts that remain rare under fine-tuning. This gives IP owners an asymmetric advantage: new prompt compositions can be generated after deployment, while attackers must anticipate and suppress a much broader space of fingerprints. Across 6 model families (FLUX, Kandinsky, SD1.5/2.1/3.0/XL) and 13 fine-tuned variants, our Bayesian attribution framework enables controlled-risk lineage decisions, with all variants satisfying the dominance criterion.
Comments: CVPR 2026
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2604.16363 [cs.CR]
(or arXiv:2604.16363v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2604.16363
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From: Junhoo Lee [view email]
[v1] Fri, 20 Mar 2026 15:43:19 UTC (21,642 KB)
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