Identifying Models Behind Text-to-Image Leaderboards

• URL: http://arxiv.org/abs/2601.09647v1
• Date: Wed, 14 Jan 2026 17:30:58 GMT
• Title: Identifying Models Behind Text-to-Image Leaderboards
• Abstract summary: Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online.<n>In this work, we show that such anonymity can be easily broken.<n>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.
• Score: 24.40199910521865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models [99.66311024852651]
  Existing model fingerprinting methods lack robustness against collusion attacks.<n>We propose a robust fingerprinting method for T2I models with anti-collusion capabilities.<n>Our method achieves high fidelity and robustness across multiple T2I image generation and editing tasks, with fingerprint extraction accuracy exceeding 99.5%.
  arXiv  Detail & Related papers  (2026-06-11T07:12:13Z)
• FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges [85.24983823102262]
  We propose a structured methodology for evaluating text-to-image (T2I) models and vision language models (VLMs)<n>We test whether VLMs can identify 27 specific failure modes in the images generated by T2I models conditioned on challenging prompts.<n>Our findings suggest that current metrics are insufficient to capture these nuanced errors.
  arXiv  Detail & Related papers  (2025-12-01T19:46:03Z)
• Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security [24.40199910521865]
  We show that rank manipulation in text-to-image leaderboards is easier than previously recognized.<n>Our results indicate that rank manipulation in text-to-image leaderboards is easier than previously recognized.
  arXiv  Detail & Related papers  (2025-10-07T23:53:41Z)
• AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models [58.85362281293525]
  We introduce AcT2I, a benchmark designed to evaluate the performance of T2I models in generating images from action-centric prompts.<n>We experimentally validate that leading T2I models do not fare well on AcT2I.<n>We build upon this by developing a training-free, knowledge distillation technique utilizing Large Language Models to address this limitation.
  arXiv  Detail & Related papers  (2025-09-19T16:41:39Z)
• PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting [31.35160142315478]
  We introduce PromptEnhancer, a novel and universal prompt rewriting framework for text-to-image (T2I) models.<n>Unlike prior methods that rely on model-specific fine-tuning or implicit reward signals like image-reward scores, our framework decouples the rewriter from the generator.<n>Experiments on the HunyuanImage 2.1 model demonstrate that PromptEnhancer significantly improves image-text alignment across a wide range of semantic and compositional challenges.
  arXiv  Detail & Related papers  (2025-09-04T16:46:10Z)
• PromptLA: Towards Integrity Verification of Black-box Text-to-Image Diffusion Models [17.12906933388337]
  Malicious actors can fine-tune text-to-image (T2I) diffusion models to generate illegal content.<n>We propose a novel prompt selection algorithm based on learning automaton (PromptLA) for efficient and accurate verification.
  arXiv  Detail & Related papers  (2024-12-20T07:24:32Z)
• Information Theoretic Text-to-Image Alignment [49.396917351264655]
  Mutual Information (MI) is used to guide model alignment.<n>Our method uses self-supervised fine-tuning and relies on a point-wise (MI) estimation between prompts and images.<n>Our analysis indicates that our method is superior to the state-of-the-art, yet it only requires the pre-trained denoising network of the T2I model itself to estimate MI.
  arXiv  Detail & Related papers  (2024-05-31T12:20:02Z)
• ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning [57.91881829308395]
  Identity-preserving text-to-image generation (ID-T2I) has received significant attention due to its wide range of application scenarios like AI portrait and advertising. We present textbfID-Aligner, a general feedback learning framework to enhance ID-T2I performance.
  arXiv  Detail & Related papers  (2024-04-23T18:41:56Z)
• Improving Text-to-Image Consistency via Automatic Prompt Optimization [26.2587505265501]
  We introduce a T2I optimization-by-prompting framework, OPT2I, to improve prompt-image consistency in T2I models. Our framework starts from a user prompt and iteratively generates revised prompts with the goal of maximizing a consistency score.
  arXiv  Detail & Related papers  (2024-03-26T15:42:01Z)
• Discriminative Probing and Tuning for Text-to-Image Generation [129.39674951747412]
  Text-to-image generation (T2I) often faces text-image misalignment problems such as relation confusion in generated images. We propose bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment.
  arXiv  Detail & Related papers  (2024-03-07T08:37:33Z)
• Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models [67.68871360210208]
  Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency.<n>We propose a novel fine-tuning objective, dubbed Direct Consistency Optimization, which controls the deviation between fine-tuning and pretrained models.<n>We show that our approach achieves better prompt fidelity and subject fidelity than those post-optimized for merging regular fine-tuned models.
  arXiv  Detail & Related papers  (2024-02-19T09:52:41Z)
• If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection [53.320946030761796]
  diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt. We show that large T2I diffusion models are more faithful than usually assumed, and can generate images faithful to even complex prompts. We introduce a pipeline that generates candidate images for a text prompt and picks the best one according to an automatic scoring system.
  arXiv  Detail & Related papers  (2023-05-22T17:59:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.