mlsanityData to 14 Oct 2026

Identifying Models Behind Text-to-Image Leaderboards

arXiv 2601.09647 ↗First posted 14 Jan 2026cs.CV cs.CR cs.LGcited by 0

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Abstract (arXiv)

Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online. To compare model quality, voting-based leaderboards have become the standard, relying on anonymized model outputs for fairness. In this work, we show that such anonymity can be easily broken. We find that generations from each T2I model form distinctive clusters in the image embedding space, enabling accurate deanonymization without prompt control or training data. Using 22 models and 280 prompts (150K images), our centroid-based method achieves high accuracy and reveals systematic model-specific signatures. We further introduce a prompt-level distinguishability metric and conduct large-scale analyses showing how certain prompts can lead to near-perfect distinguishability. Our findings expose fundamental security flaws in T2I leaderboards and motivate stronger anonymization defenses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 59 references that are in the map. First posted on arXiv 14 Jan 2026.

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References · 42

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30 more
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