Detecting Unauthorized Data Usage in Text-to-Image Diffusion Models

Core Concepts

A method for detecting unauthorized data usage in text-to-image diffusion models by planting injected memorization into the models trained on the protected dataset.

Abstract

The paper proposes a method called DIAGNOSIS for detecting unauthorized data usage in the training or fine-tuning process of text-to-image diffusion models. The key idea is to plant unique behaviors, called injected memorization, into the models trained on the protected dataset by modifying the dataset. This is done by adding stealthy transformations (signal function) to a subset of the protected images. The models trained or fine-tuned on the modified dataset will memorize the signal function, which can then be detected using a binary classifier.

The paper defines two types of injected memorization: unconditional and trigger-conditioned. The former is always activated, while the latter is only activated when a specific text trigger is used. The paper then describes the overall pipeline, including the dataset coating phase and the detection phase.

Experiments are conducted on mainstream text-to-image diffusion models (Stable Diffusion and VQ Diffusion) with different training or fine-tuning methods (LoRA, DreamBooth, and standard training). The results show that DIAGNOSIS can effectively detect unauthorized data usage with 100% accuracy, while having a small influence on the generation quality of the models.

The paper also discusses the influence of different warping strengths and coating rates on the injected memorization and the generation quality. It compares DIAGNOSIS to an existing method and demonstrates its superior performance.

Stats

The average memorization strength for models with unauthorized data usage is 91.2%, while it is only 5.1% for models without unauthorized data usage.
The FID for the model with unconditional injected memorization is 218.28, compared to 199.29 for the standard model without any injected memorization.
The FID for the model with trigger-conditioned injected memorization is 239.03 when the text trigger is added, compared to 209.16 without the text trigger.

Quotes

"Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized data usage during the training or fine-tuning process."
"Existing work such as Glaze (Shan et al., 2023) prevents unauthorized usage of data by adding carefully calculated perturbations to safeguarded artworks, causing text-to-image diffusion models to learn significantly different image styles. While it prevents the unauthorized usages, it also makes authorized training impossible."
"Different from the sample-level memorization, in this work, we focus on diffusion models' memorization on specific elements in the training data and propose an approach for detecting unauthorized data usages via planting the injected element-level memorizations into the model trained or fine-tuned on the protected dataset by modifying the protected training data."

Key Insights Distilled From

DIAGNOSIS

by Zhenting Wan... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2307.03108.pdf

Deeper Inquiries

How can the proposed method be extended to handle scenarios where the infringer collects training or fine-tuning data from multiple sources, some of which may not be protected by the protector

To handle scenarios where the infringer collects training or fine-tuning data from multiple sources, the proposed method can be extended by incorporating a more sophisticated data tracing mechanism. This mechanism can involve tagging the protected data with unique identifiers or watermarks that can be tracked across different datasets. By embedding these identifiers in the protected data, the protector can trace the usage of the data even when it is collected from multiple sources. Additionally, the method can be enhanced to include a cross-validation process that compares the usage of the protected data across different datasets to identify unauthorized usages.

What are the potential limitations of the injected memorization approach, and how can it be further improved to be more robust and less intrusive to the authorized training or fine-tuning process

One potential limitation of the injected memorization approach is the risk of interfering with the authorized training or fine-tuning process of the models. To address this, the approach can be further improved by developing more advanced techniques for injecting memorization that have minimal impact on the model's performance and generation quality. This can involve optimizing the selection of injected content and the strength of the memorization to ensure that it does not compromise the model's functionality. Additionally, incorporating adaptive mechanisms that adjust the injected memorization based on the model's behavior during training can help make the approach more robust and less intrusive.

Given the growing concerns around the responsible development of text-to-image diffusion models, how can the insights from this work be applied to address other ethical and security challenges in this domain, such as mitigating biases, preventing misuse, or enhancing transparency

The insights from this work can be applied to address various ethical and security challenges in the development of text-to-image diffusion models. For mitigating biases, the method can be adapted to detect and prevent the unauthorized use of biased training data, thereby promoting fairness and inclusivity in model development. To prevent misuse, the approach can be utilized to identify unauthorized data usages that may lead to harmful or unethical outcomes. Enhancing transparency can be achieved by using the method to track the origin and usage of training data, ensuring accountability and ethical practices in model development. By leveraging the principles and techniques from this work, stakeholders can promote responsible and ethical development practices in the text-to-image diffusion model domain.