FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing

TL;DR

FreezeAsGuard is a technique that selectively freezes parts of diffusion models to prevent illegal adaptations, effectively reducing misuse while preserving legal functionalities.

Contribution

It introduces a novel tensor freezing method that irreversibly mitigates illegal model adaptations with minimal impact on legal uses.

Findings

37% stronger mitigation of illegal adaptations

Less than 5% impact on legal adaptations

Effective across multiple application domains

Abstract

Text-to-image diffusion models can be fine-tuned in custom domains to adapt to specific user preferences, but such adaptability has also been utilized for illegal purposes, such as forging public figures' portraits, duplicating copyrighted artworks and generating explicit contents. Existing work focused on detecting the illegally generated contents, but cannot prevent or mitigate illegal adaptations of diffusion models. Other schemes of model unlearning and reinitialization, similarly, cannot prevent users from relearning the knowledge of illegal model adaptation with custom data. In this paper, we present FreezeAsGuard, a new technique that addresses these limitations and enables irreversible mitigation of illegal adaptations of diffusion models. Our approach is that the model publisher selectively freezes tensors in pre-trained diffusion models that are critical to illegal model adaptations, to mitigate the fine-tuned model's representation power in illegal adaptations, but minimize the impact on other legal adaptations. Experiment results in multiple text-to-image application domains show that FreezeAsGuard provides 37% stronger power in mitigating illegal model adaptations compared to competitive baselines, while incurring less than 5% impact on legal model adaptations. The source code is available at: https://github.com/pittisl/FreezeAsGuard.