Verify Claimed Text-to-Image Models via Boundary-Aware Prompt Optimization

• URL: http://arxiv.org/abs/2603.26328v1
• Date: Fri, 27 Mar 2026 11:46:27 GMT
• Title: Verify Claimed Text-to-Image Models via Boundary-Aware Prompt Optimization
• Abstract summary: False claims of using official models can mislead users and harm model owners' reputations.<n>Existing methods address this by using verification prompts generated by official model owners.<n>We propose a reference-free T2I model verification method called Boundary-aware Prompt Optimization.
• Score: 29.365081306586237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Text-to-Image (T2I) generation becomes widespread, third-party platforms increasingly integrate multiple model APIs for convenient image creation. However, false claims of using official models can mislead users and harm model owners' reputations, making model verification essential to confirm whether an API's underlying model matches its claim. Existing methods address this by using verification prompts generated by official model owners, but the generation relies on multiple reference models for optimization, leading to high computational cost and sensitivity to model selection. To address this problem, we propose a reference-free T2I model verification method called Boundary-aware Prompt Optimization (BPO). It directly explores the intrinsic characteristics of the target model. The key insight is that although different T2I models produce similar outputs for normal prompts, their semantic boundaries in the embedding space (transition zones between two concepts such as "corgi" and "bagel") are distinct. Prompts near these boundaries generate unstable outputs (e.g., sometimes a corgi and sometimes a bagel) on the target model but remain stable on other models. By identifying such boundary-adjacent prompts, BPO captures model-specific behaviors that serve as reliable verification cues for distinguishing T2I models. Experiments on five T2I models and four baselines demonstrate that BPO achieves superior verification accuracy.

Related papers

• Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation [38.697895774925136]
  Collapsed generation is an intrinsic, model-dependent property of the learned generation process.<n>We show collapsed generation fingerprints can distinguish different source models with low confusion.<n>These fingerprints remain verifiable in fine-tuned derivatives and under common and adaptive model- or query-level obfuscations.
  arXiv  Detail & Related papers  (2026-08-12T07:12:38Z)
• Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs [63.82840470917859]
  We show that the decoding mechanism of dLLMs can be used as a powerful tool for model attribution.<n>We propose a novel information extraction scheme called the Directed Decoding Map (DDM), which captures structural relationships between decoding steps and better reveals model-specific behaviors.
  arXiv  Detail & Related papers  (2025-10-02T06:25:10Z)
• Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning [58.16354555208417]
  PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively.<n>The lack of a Unified Face Attack Detection model to simultaneously handle attacks in these two categories is mainly attributed to two factors.<n>We present a novel Visual-Language Model-based Hierarchical Prompt Tuning Framework that adaptively explores multiple classification criteria from different semantic spaces.
  arXiv  Detail & Related papers  (2025-05-19T16:35:45Z)
• DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging [32.97010533998294]
  We introduce a style-promptable image generation pipeline which can accurately generate arbitrary-style images under the control of style vectors.<n>Based on this design, we propose the score distillation based model merging paradigm (DMM), compressing multiple models into a single versatile T2I model.<n>Our experiments demonstrate that DMM can compactly reorganize the knowledge from multiple teacher models and achieve controllable arbitrary-style generation.
  arXiv  Detail & Related papers  (2025-04-16T15:09:45Z)
• 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)
• SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models [77.80595722480074]
  SleeperMark is a framework designed to embed resilient watermarks into T2I diffusion models.<n>It guides the model to disentangle the watermark information from the semantic concepts it learns.<n>Our experiments demonstrate the effectiveness of SleeperMark across various types of diffusion models.
  arXiv  Detail & Related papers  (2024-12-06T08:44:18Z)
• One Prompt to Verify Your Models: Black-Box Text-to-Image Models Verification via Non-Transferable Adversarial Attacks [8.616538774420812]
  We propose T2I model verification, which aims to determine whether a black-box target model is identical to a given white-box reference T2I model.<n>Intrepid prompt is an adversarial prompt for the target model without transferability for other models.<n>IntrepidPrompt consistently achieves over 90% accuracy across various T2I models.
  arXiv  Detail & Related papers  (2024-10-30T06:17:20Z)
• Truncated Consistency Models [57.50243901368328]
  Training consistency models requires learning to map all intermediate points along PF ODE trajectories to their corresponding endpoints.<n>We empirically find that this training paradigm limits the one-step generation performance of consistency models.<n>We propose a new parameterization of the consistency function and a two-stage training procedure that prevents the truncated-time training from collapsing to a trivial solution.
  arXiv  Detail & Related papers  (2024-10-18T22:38:08Z)
• 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)
• Speculative Decoding with Big Little Decoder [108.95187338417541]
  Big Little Decoder (BiLD) is a framework that can improve inference efficiency and latency for a wide range of text generation applications. On an NVIDIA T4 GPU, our framework achieves a speedup of up to 2.12x speedup with minimal generation quality degradation. Our framework is fully plug-and-play and can be applied without any modifications in the training process or model architecture.
  arXiv  Detail & Related papers  (2023-02-15T18:55:29Z)
• Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time [69.7693300927423]
  We show that averaging the weights of multiple models fine-tuned with different hyper parameter configurations improves accuracy and robustness. We show that the model soup approach extends to multiple image classification and natural language processing tasks.
  arXiv  Detail & Related papers  (2022-03-10T17:03:49Z)
• ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection [9.106864924968251]
  ModelDiff is a testing-based approach to deep learning model similarity comparison. A study on mobile deep learning apps has shown the feasibility of ModelDiff on real-world models.
  arXiv  Detail & Related papers  (2021-06-11T15:16:18Z)

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.