Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion? (2312.00084v2)

Abstract: Stable Diffusion has established itself as a foundation model in generative AI artistic applications, receiving widespread research and application. Some recent fine-tuning methods have made it feasible for individuals to implant personalized concepts onto the basic Stable Diffusion model with minimal computational costs on small datasets. However, these innovations have also given rise to issues like facial privacy forgery and artistic copyright infringement. In recent studies, researchers have explored the addition of imperceptible adversarial perturbations to images to prevent potential unauthorized exploitation and infringements when personal data is used for fine-tuning Stable Diffusion. Although these studies have demonstrated the ability to protect images, it is essential to consider that these methods may not be entirely applicable in real-world scenarios. In this paper, we systematically evaluate the use of perturbations to protect images within a practical threat model. The results suggest that these approaches may not be sufficient to safeguard image privacy and copyright effectively. Furthermore, we introduce a purification method capable of removing protected perturbations while preserving the original image structure to the greatest extent possible. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.

• The paper demonstrates a comprehensive evaluation of protective perturbations under various fine-tuning methods and image transformations.
• It reveals that the protection efficacy significantly depends on the ratio of safeguarded to unsafeguarded images, underscoring the need for widespread application.
• The study introduces GrIDPure, a novel grid-based purification method that effectively removes adversarial noise while preserving original image details.

Evaluating the Efficacy of Protective Perturbations Against Stable Diffusion Exploitation

Introduction

The utilization of generative AI, particularly Stable Diffusion models in artistic and personal imaging applications, has significantly increased. Accompanying its widespread adoption are growing concerns about image privacy and copyright infringement. Protective perturbations have been proposed as a countermeasure to inhibit unauthorized image exploitation by altering images in an imperceptible manner. This paper explores the practical viability of these protective strategies within a realistic scenario, evaluating their effectiveness across various conditions and introducing an innovative method, GrIDPure, for removing such perturbations.

Background & Related Works

At the core of Stable Diffusion's success is its architecture, which efficiently generates high-resolution images. The model's ability to fine-tune using small datasets has, however, raised significant privacy and copyright concerns. Previous research has focused on inserting protective adversarial perturbations to preempt unauthorized use, showing promise in deterring exploitation. Nonetheless, questions remain about the real-world applicability and resilience of these methods to different attack vectors, including state-of-the-art adversarial purification techniques.

Threat Model

A practical threat model is essential for assessing the robustness of protection mechanisms against image exploitation. The model considers two primary actors: the image protector, who seeks to apply protective perturbations without significantly altering the image, and the image exploiter, who aims to use these images for training generative models. This model helps in evaluating the effectiveness of protection methods under various real-world conditions, including different fine-tuning approaches and potential image transformations.

Evaluate the Protective Perturbation

The paper's evaluation highlights several findings:

• The effectiveness of protective perturbations varies significantly across different fine-tuning methods, with some methods showing substantial vulnerability.
• The ratio of protected to unprotected images significantly influences the effectiveness of protection, indicating a need for widespread application of perturbations to achieve meaningful security.
• Natural transformations such as JPEG compression and Gaussian blur can undermine protection, suggesting a lack of robustness in current protective strategies.

Defense: GrIDPure

In response to the limitations of existing protective perturbations, the paper introduces GrIDPure, an advanced purification method that effectively removes adversarial noise while preserving the original image structure. GrIDPure operates by dividing the image into multiple grids, purifying each individually, and then intelligently merging them. This approach demonstrates superior ability to bypass protections and restore image learnability for Stable Diffusion models.

Conclusion

This research presents a comprehensive analysis of the application and resilience of protective perturbations against Stable Diffusion models. While the protective methods evaluated show varying degrees of success, their overall effectiveness in real-world scenarios is questioned. The introduction of GrIDPure represents a significant advancement in the field, offering a more reliable method for circumventing existing protections. Future work is needed to develop even more robust protection mechanisms and to further explore the implications of adversarial attacks and defenses in the context of generative AI.