Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?

• URL: http://arxiv.org/abs/2503.09122v1
• Date: Wed, 12 Mar 2025 07:15:16 GMT
• Title: Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?
• Authors: Yuechen Xie, Jie Song, Huiqiong Wang, Mingli Song,
• Abstract summary: High-quality open-source text-to-image models have lowered the threshold for obtaining photorealistic images significantly.<n>Suspects may use synthetic data generated by these generative models to train models for specific tasks without permission.<n>We propose the first method to this important yet unresolved issue, called Training data Provenance Verification (TrainProVe)<n>We validate the efficacy of TrainProVe across four text-to-image models (Stable Diffusion v1.4, latent consistency model, PixArt-$alpha$, and Stable Cascade)
• Score: 36.827310918094874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality open-source text-to-image models have lowered the threshold for obtaining photorealistic images significantly, but also face potential risks of misuse. Specifically, suspects may use synthetic data generated by these generative models to train models for specific tasks without permission, when lacking real data resources especially. Protecting these generative models is crucial for the well-being of their owners. In this work, we propose the first method to this important yet unresolved issue, called Training data Provenance Verification (TrainProVe). The rationale behind TrainProVe is grounded in the principle of generalization error bound, which suggests that, for two models with the same task, if the distance between their training data distributions is smaller, their generalization ability will be closer. We validate the efficacy of TrainProVe across four text-to-image models (Stable Diffusion v1.4, latent consistency model, PixArt-$\alpha$, and Stable Cascade). The results show that TrainProVe achieves a verification accuracy of over 99\% in determining the provenance of suspicious model training data, surpassing all previous methods. Code is available at https://github.com/xieyc99/TrainProVe.

Related papers

• Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing [21.52641337754884]
  A type of adversarial attack can manipulate the behavior of machine learning models through contaminating their training dataset. We introduce our EDT model, an textbfEfficient, textbfData-free, textbfTraining-free backdoor attack method. Inspired by model editing techniques, EDT injects an editing-based lightweight codebook into the backdoor of large pre-trained models.
  arXiv  Detail & Related papers  (2024-10-23T20:32:14Z)
• 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)
• Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine? [17.714589429503675]
  We propose an injection-free training data attribution method for text-to-image models. Our approach involves developing algorithms to uncover distinct samples and using them as inherent watermarks. Our experiments demonstrate that our method achieves an accuracy of over 80% in identifying the source of a suspicious model's training data.
  arXiv  Detail & Related papers  (2024-09-24T06:23:43Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Tools for Verifying Neural Models' Training Data [29.322899317216407]
  "Proof-of-Training-Data" allows a model trainer to convince a Verifier of the training data that produced a set of model weights. We show experimentally that our verification procedures can catch a wide variety of attacks.
  arXiv  Detail & Related papers  (2023-07-02T23:27:00Z)
• Masked Diffusion Models Are Fast Distribution Learners [32.485235866596064]
  Diffusion models are commonly trained to learn all fine-grained visual information from scratch. We show that it suffices to train a strong diffusion model by first pre-training the model to learn some primer distribution. Then the pre-trained model can be fine-tuned for various generation tasks efficiently.
  arXiv  Detail & Related papers  (2023-06-20T08:02:59Z)
• TRAK: Attributing Model Behavior at Scale [79.56020040993947]
  We present TRAK (Tracing with Randomly-trained After Kernel), a data attribution method that is both effective and computationally tractable for large-scale, differenti models.
  arXiv  Detail & Related papers  (2023-03-24T17:56:22Z)
• Defending against Model Stealing via Verifying Embedded External Features [90.29429679125508]
  adversaries can steal' deployed models even when they have no training samples and can not get access to the model parameters or structures. We explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified emphexternal features. Our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process.
  arXiv  Detail & Related papers  (2021-12-07T03:51:54Z)

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.