TwinMark: A Unified Watermark for Provable Survival Under Feature and Logit Distillation
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:TwinMark: A Unified Watermark for Provable Survival Under Feature and Logit Distillation
Abstract:We propose TwinMark, a watermarking scheme that reads a single SHAKE128 secret through two complementary linear functionals of model-output summaries: a covariance projector against the carrier-set covariance (cov-Feat) and a class-conditional Fisher-aligned linear carrier decoded from class-mean logits (cc-FALC). The two readouts share one bit vector and cover the two extraction surfaces of a deployed vision model: a classifier API attacked by KL knowledge distillation (KD) (Std. KL-KD), and a representation-only host attacked by feature-matching KD (FM-KD). Each readout admits a teacher-measurable a posteriori certificate that lower-bounds post-distillation detection power, and the two channels combine under a regime-restricted OR rule whose test statistic (calibrated null or bit vote) is selected by the exposed surface. cov-Feat admits a rank-blind operator-norm certificate, cc-FALC admits a centered-logit-gap certificate that decouples bit capacity from class count: at K=1024 in m=100 classes (a 10.24x over-encoding), the bit-vote attains z=23.0 sigma at a teacher-accuracy cost of +0.9+-0.2%p. Across 13 attacks on CIFAR-10, CIFAR-100, and Mini-ImageNet, TwinMark verifies on every cell whose post-attack model retains task utility, survives cross-architecture distillation onto ResNet-18/50, VGG-16, and MobileNet-V3, and ports to GNSS few-shot, VOC detection, ISIC segmentation, and STL-10 SimCLR.
| Comments: | 10 figures, 46 pages |
| Subjects: | Machine Learning (cs.LG) |
| MSC classes: | 62N02, 03B42, 68T30 |
| ACM classes: | H.1.1; H.3.3; I.2.4 |
| Cite as: | arXiv:2609.19011 [cs.LG] |
| (or arXiv:2609.19011v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19011
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Stable and Faithful Explanations for Knowledge Tracing
Sep 25
-
SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
Sep 25
-
CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation
Sep 25
-
CARE: Condition-Aware Representation Regularization for Diffusion Models
Sep 25
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.