PID: Prompt-Independent Data Protection Against Latent Diffusion Models

TL;DR

This paper introduces PID, a novel prompt-independent method to protect personal data from being exploited by Latent Diffusion Models, especially when textual prompts differ, enhancing privacy safeguards efficiently.

Contribution

The paper reveals limitations of existing defenses under prompt discrepancies and proposes PID, a simple, effective, and computationally efficient visual encoder manipulation technique for data privacy protection.

Findings

PID effectively defends against LDM misuse with less computation

Discrepancies in textual prompts weaken existing defenses

Manipulating the visual encoder enhances privacy protection

Abstract

The few-shot fine-tuning of Latent Diffusion Models (LDMs) has enabled them to grasp new concepts from a limited number of images. However, given the vast amount of personal images accessible online, this capability raises critical concerns about civil privacy. While several previous defense methods have been developed to prevent such misuse of LDMs, they typically assume that the textual prompts used by data protectors exactly match those employed by data exploiters. In this paper, we first empirically demonstrate that breaking this assumption, i.e., in cases where discrepancies exist between the textual conditions used by protectors and exploiters, could substantially reduce the effectiveness of these defenses. Furthermore, considering the visual encoder's independence from textual prompts, we delve into the visual encoder and thoroughly investigate how manipulating the visual encoder affects the few-shot fine-tuning process of LDMs. Drawing on these insights, we propose a simple yet effective method called \textbf{Prompt-Independent Defense (PID)} to safeguard privacy against LDMs. We show that PID can act as a strong privacy shield on its own while requiring significantly less computational power. We believe our studies, along with the comprehensive understanding and new defense method, provide a notable advance toward reliable data protection against LDMs.