MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection (2405.19458v3)

Abstract: Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, particularly in sensitive domains such as medical imaging. We hypothesize that this memorization stems from the overparameterization of deep models and propose that regularizing model capacity during fine-tuning can mitigate this issue. Firstly, we empirically show that regulating the model capacity via Parameter-efficient fine-tuning (PEFT) mitigates memorization to some extent, however, it further requires the identification of the exact parameter subsets to be fine-tuned for high-quality generation. To identify these subsets, we introduce a bi-level optimization framework, MemControl, that automates parameter selection using memorization and generation quality metrics as rewards during fine-tuning. The parameter subsets discovered through MemControl achieve a superior tradeoff between generation quality and memorization. For the task of medical image generation, our approach outperforms existing state-of-the-art memorization mitigation strategies by fine-tuning as few as 0.019% of model parameters. Moreover, we demonstrate that the discovered parameter subsets are transferable to non-medical domains. Our framework is scalable to large datasets, agnostic to reward functions, and can be integrated with existing approaches for further memorization mitigation. To the best of our knowledge, this is the first study to empirically evaluate memorization in medical images and propose a targeted yet universal mitigation strategy. The code is available at https://github.com/Raman1121/Diffusion_Memorization_HPO

• The paper presents a novel bi-level optimization framework that balances parameter-efficient fine-tuning with reduced data memorization.
• It demonstrates a significant reduction in memorized training data while preserving generation quality, validated on medical imaging benchmarks.
• The approach shows robustness and transferability across datasets, offering privacy-enhancing techniques for sensitive applications.

MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection

Abstract

Memorization in diffusion models poses critical privacy and ethical challenges, particularly when applied in sensitive fields like medical imaging. The paper "MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection" addresses this issue by proposing a novel bi-level optimization framework designed to balance generation quality and memorization mitigation during the fine-tuning of diffusion models.

Introduction

Diffusion models have demonstrated exceptional capabilities in generating high-quality content across various modalities such as images, audio, and graphs. These models are gaining prominence in commercial applications and research, notably in domains requiring high-quality synthetic data generation. However, an intrinsic challenge persists: the tendency of these models to memorize training data, hence undermining data privacy and raising ethical concerns. This problem is particularly exacerbated in healthcare applications, where the disclosure of training data could lead to serious privacy violations.

Methodology

The proposed solution hypothesizes that the root cause of memorization lies in the overcapacity of deep neural networks. The authors assert that by regularizing model capacity, one can effectively mitigate memorization. Parameter-efficient fine-tuning (PEFT) methods provide an avenue to control model capacity by selectively updating specific subsets of parameters. However, identifying the optimal subset for balancing generation quality and memorization is complex and remains an open challenge.

To address this, the authors introduce a bi-level optimization framework. The inner loop fine-tunes the model assuming a given parameter subset specified by a binary mask, while the outer loop involves searching for the optimal mask based on memorization and generation quality metrics. The optimization targets a Pareto front to identify dominant configurations that minimize both memorization and quality degradation.

Experimental Setup

Experiments were carried out using the MIMIC medical imaging dataset, consisting primarily of chest X-rays and associated text. The primary evaluation metrics included:

• Fréchet Inception Distance (FID) to measure the generation quality.
• Nearest Neighbor Distance (AMD) and an Extraction Attack to quantify memorization.
• BioViL-T score to evaluate the alignment between generated images and text prompts.

The authors compared MemControl with baseline approaches such as full fine-tuning, standard PEFT methods like SV-DIFF and DiffFit, and existing mitigation strategies like Random Word Addition (RWA) and Threshold Mitigation.

Results

The experimental results underscore the efficacy of MemControl. Specifically, MemControl achieved superior performance compared to both standard PEFT methods and traditional mitigation techniques across all evaluation metrics. For instance, when compared with full fine-tuning, MemControl reduced the number of memorized images from 356 to 28 while maintaining high generation quality (lower FID score).

Furthermore, the masks learned using MemControl demonstrated robustness and transferability across different datasets. For instance, when applied to the Imagenette dataset, the masks originally optimized on MIMIC continued to yield commendable performance, suggesting that the discovered fine-tuning strategies can generalize across domains.

Implications and Speculations

The findings have significant implications for the deployment of diffusion models in sensitive applications. By effectively balancing memorization and generation quality, MemControl enables the responsible use of generative models in domains like healthcare, where data privacy is paramount. Moreover, the transferability of the optimization strategy suggests that once an optimal mask is found, it can be applied across various tasks, thereby reducing the computational burden associated with repeated searches.

Looking forward, further exploration into adaptive PEFT strategies for dynamic adjustment during fine-tuning could provide additional benefits. Integrating more complex memorization metrics or hybrid models that incorporate both training time and inference time interventions could also enhance the robustness of the approach.

Conclusion

The paper presents a sophisticated solution to a pressing problem in the deployment of diffusion models. By focusing on parameter-efficient fine-tuning and leveraging a bi-level optimization strategy, the authors offer a method that mitigates memorization while preserving generation quality. The success and transferability of the proposed approach underline its potential for broad application, offering a valuable tool for the responsible deployment of generative models in privacy-sensitive domains.