To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now (2310.11868v4)

Abstract: The recent advances in diffusion models (DMs) have revolutionized the generation of realistic and complex images. However, these models also introduce potential safety hazards, such as producing harmful content and infringing data copyrights. Despite the development of safety-driven unlearning techniques to counteract these challenges, doubts about their efficacy persist. To tackle this issue, we introduce an evaluation framework that leverages adversarial prompts to discern the trustworthiness of these safety-driven DMs after they have undergone the process of unlearning harmful concepts. Specifically, we investigated the adversarial robustness of DMs, assessed by adversarial prompts, when eliminating unwanted concepts, styles, and objects. We develop an effective and efficient adversarial prompt generation approach for DMs, termed UnlearnDiffAtk. This method capitalizes on the intrinsic classification abilities of DMs to simplify the creation of adversarial prompts, thereby eliminating the need for auxiliary classification or diffusion models. Through extensive benchmarking, we evaluate the robustness of widely-used safety-driven unlearned DMs (i.e., DMs after unlearning undesirable concepts, styles, or objects) across a variety of tasks. Our results demonstrate the effectiveness and efficiency merits of UnlearnDiffAtk over the state-of-the-art adversarial prompt generation method and reveal the lack of robustness of current safetydriven unlearning techniques when applied to DMs. Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attack. WARNING: There exist AI generations that may be offensive in nature.

• The paper reveals that current unlearning approaches in diffusion models fail to block adversarial prompts from generating unsafe images.
• It introduces UnlearnDiffAtk, an efficient method that uses iterative text perturbations via diffusion classifiers to expose model vulnerabilities.
• Experimental results across five models demonstrate high attack success rates, underscoring the urgent need for more robust unlearning protocols.

UnlearnDiffAtk: Efficiency Issues in Unlearning Methods for Secure Image Generation

The paper entitled "To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy to Generate Unsafe Images ... For Now" explores the efficacy and challenges of safety-driven unlearning in diffusion models (DMs). The authors examine the robustness of these models to adversarial prompts, revealing limitations in current approaches despite ongoing advancements in DMs for text-to-image synthesis. This analysis is situated within the broader context of machine unlearning (MU), which seeks to eliminate specific data influences without entirely reconstructing models.

Background and Motivation

Diffusion models have rapidly advanced in generating high-quality images from textual descriptions. However, the risks associated with generating harmful or inappropriate content have come to the forefront. Techniques developed to mitigate these risks often fall short, leading to the need for rigorous evaluation frameworks. This paper specifically addresses the evaluation of models designed to unlearn harmful concepts, styles, and objects, aiming to ensure the robustness of these systems against adversarial intrusion.

Methodology

The authors introduce an innovative approach, termed UnlearnDiffAtk, which leverages the intrinsic classification capability of diffusion models to generate adversarial prompts without the computational burden of auxiliary models. This method simplifies adversarial prompt generation by framing the problem in terms of diffusion classifiers. The adversarial prompt construction iteratively refines text perturbations to exploit vulnerabilities in unlearned diffusion models. This approach centers on binary classification, simplifying the evaluation of discrepancies between model predictions and their intended unlearning goals.

Experimental Evaluation

Extensive benchmarking on five different unlearned DMs reveals significant insights. Despite employing state-of-the-art unlearning techniques for various tasks, these models demonstrate notable deficiencies:

1. Concept Unlearning: Against inappropriate inputs, such as nudity or violence prompts, DMs employing unlearning techniques exhibit vulnerabilities, with adversarial attacks achieving high success rates. The paper reports pre-attack success rates (pre-ASR) increased by substantial post-attack success rates (post-ASR), underscoring the ineffectiveness of existing methods against carefully crafted adversarial prompts.
2. Style and Object Unlearning: Similar shortcomings are observed in models designed to resist prompts focusing on unlearning specific artistic styles or objects. The diffusion classifier-based method resulted in considerable success rates in bypassing safety checks, indicating a need for further exploration in the development of unlearned DMs.

Implications and Future Directions

This paper highlights the critical need for more robust, computationally efficient unlearning methodologies in generative AI models. The authors urge for a reevaluation of current unlearning strategies, given the vulnerabilities exposed in this research. By proposing UnlearnDiffAtk, they provide a pathway towards more reliable metrics for evaluating the safety of DMs. This work invites further research into integrating adversarial robustness directly into the training processes of generative models, emphasizing the need to mitigate potential harm from unsafe content generation.

Future work could investigate more sophisticated mechanisms for embedding unlearning protocols directly into model architectures, potentially enhancing their resilience without undue reliance on post-hoc data filtration or cumbersome auxiliary models. The findings suggest that true safety in AI-driven image generation remains an open challenge, poised at the intersection of adversarial robustness and privacy-preserving machine learning.