aiqbench analysis
This research addresses a critical privacy vulnerability in text-to-image models by introducing a black-box probe that detects identity memorization without needing access to training data or reference photos. The study validates this method using the new NAMESAKES dataset, demonstrating its ability to distinguish between generated faces of public figures and those who are unrecognized across various model architectures.
NAMESAKES: Probing Identity Memorization in Text-to-Image Models
arXiv:2606.20155v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models generate realistic likenesses of some individuals when prompted with their names, raising privacy concerns. However, distinguishing whether a generated face is memorized or fabricated currently requires ground-truth photos, access to training data, or white-box access to model internals, limiting applicability. We introduce a fully black-box behavioral probe that distinguishes between memorized and unrecognized names, while requiring no reference photos or prior knowledge of training data. To benchmark this task, we present the NAMESAKES dataset of over one thousand names and faces of public figures spanning a wide range of fame levels, along with perturbed, less famous names. Experiments on state-of-the-art T2I models show that our probe substantially predicts identity memorization and separates memorized from unrecognized names, with further insights into differences across model families.
Abstract:Text-to-image (T2I) models generate realistic likenesses of some individuals when prompted with their names, raising privacy concerns. However, distinguishing whether a generated face is memorized or fabricated currently requires ground-truth photos, access to training data, or white-box access to model internals, limiting applicability. We introduce a fully black-box behavioral probe that distinguishes between memorized and unrecognized names, while requiring no reference photos or prior knowledge of training data. To benchmark this task, we present the NAMESAKES dataset of over one thousand names and faces of public figures spanning a wide range of fame levels, along with perturbed, less famous names. Experiments on state-of-the-art T2I models show that our probe substantially predicts identity memorization and separates memorized from unrecognized names, with further insights into differences across model families.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2606.20155 [cs.CV]
(or arXiv:2606.20155v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.20155 Focus to learn more arXiv-issued DOI via DataCite
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