Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them

Authors: Anh Tuan Bui, Thuy-Trang Vu, Long Tung Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, Dinh Phung
Primary area: generative models
Keywords: Trustworthy Generative AI, Diffusion Models, Machine Unlearning, Erasing Concepts

Abstract:
Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutral concept or just an empty text prompt. In this paper, we demonstrate that this fixed-target strategy is suboptimal, as it fails to account for the impact of erasing one concept on the others. To address this limitation, we model the concept space as a graph and empirically analyze the effects of erasing one concept on the remaining concepts. Our analysis uncovers intriguing geometric properties of the concept space, where the influence of erasing a concept is confined to a local region. Building on this insight, we propose the Adaptive Guided Erasure (AGE) method, which dynamically selects optimal target concepts tailored to each undesirable concept, minimizing unintended side effects. Experimental results show that AGE significantly outperforms state-of-the-art erasure methods on preserving unrelated concepts while maintaining effective erasure performance. Our code is published at {https://github.com/tuananhbui89/Adaptive-Guided-Erasure}.

Decision: accept (poster)
Reviewer scores: 8, 6, 5

Reviews and discussion:
<thread>
creation date:
1730443586600

thread_type:
official_review

<thread_object>
creator: reviewer_bfs9

<content>
summary:
This paper introduces the AGE method, aimed at enhancing the effectiveness of concept erasure within text-to-image diffusion models. AGE dynamically selects an optimal target concept that minimizes interference with unrelated concepts. The method models the concept space as a graph, discovering that erasure effects are localized. This insight leads to AGE's selection of closely related but non-synonymous targets for each undesirable concept, reducing unintended impacts on other model functionalities.

strengths:
1. AGE introduces an adaptive erasure approach that refines concept targeting by leveraging graph-based insights about concept space structure.
2. The method minimizes unintended impacts on unrelated concepts, which addresses a notable limitation in previous fixed-target erasure methods.
3. The findings are validated across different models, reinforcing AGE’s potential adaptability to various generative tasks and model architectures.

weaknesses:
1. The optimization procedure for selecting target concepts may be computationally demanding for models with large concept spaces, which could limit AGE’s scalability in practice.
2. The approach’s effectiveness relies on the accuracy of the concept graph’s structure. Any inaccuracies in capturing semantic relationships may affect erasure outcomes. Is there any discussion regarding this?
3. Are there any human evaluations of artistic style?
4. The proposed method doesn't achieve the SOTA, like Table 3. Any detailed discussion about this?

questions:
See above
</content>
</thread_object>

</thread>
<thread>
creation date:
1730579211582

thread_type:
official_review

<thread_object>
creator: reviewer_ec2n

<content>
summary:
This paper considers the erasure of undesirable concepts for generative models, namely diffusion models. The proposed approach, AGE, Adaptive Guided Erasure, models the concept space as a graph and locally selects a related target concept to minimize unintended side effects. Furthermore, the approach performs very well empirically. The authors demonstrate its use in several different settings.

strengths:
Concept erasure for reducing harmful content creation is clearly a highly important and impactful research area.

The proposed approach has several strengths:
* Clever and intuitive knowledge graph-based approach
* Clear and well motivated storyline for the proposed objective
* Wide variety of empirical experiments

weaknesses:
I think that the paper could be improved by the following:

* The general techniques and ideas appear as not very complex or novel (rather an application of related ideas, e.g. classic graph-based approaches) to new problems. On some level, the depth of empirical analysis makes up for this. However, the reader is left feeling as though there could have been more methodological innovation in the work. 
* The presentation of results could be a bit clearer to show more of where gains come from. For instance, in Table 1, it seems no other method comes close to the proposed one. I would love to better understand why this is?

Minor:
* typo 116: concept such as “A photo” or “ ”

questions:
How does the approach scale with the number of concepts? 
How should we think how the granularity of concepts impacts the proposed method?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730679765744

thread_type:
official_review

<thread_object>
creator: reviewer_tuut

<content>
summary:
The paper "Optimal Targets for Concept Erasure in Diffusion Models and Where to Find Them" introduces a novel approach to concept erasure in diffusion models, aimed at mitigating the generation of harmful content by selectively unlearning undesirable concepts. The authors critique the existing fixed-target strategy, which maps undesirable concepts to a generic target, as suboptimal due to its failure to consider the impact on other concepts. Instead, they propose modeling the concept space as a graph to analyze the effects of erasing one concept on others, revealing that the impact is localized.
The paper's key contributions include:
Empirical Evaluation of Concept Space: The authors present a novel empirical evaluation of the concept space's structure and geometric properties, highlighting the locality of the impact of erasing one concept on another.
Analysis of Target Concept Selection: They analyze how the choice of target concepts affects erasure effectiveness and the preservation of benign concepts, identifying that optimal targets should be closely related but not synonyms to the concept being erased.
Adaptive Guided Erasure (AGE) Method: Based on their analysis, the authors propose the AGE method, which dynamically selects optimal target concepts for each undesirable concept using a minimax optimization problem. This method models target concepts as a learned mixture of multiple single concepts, allowing for a continuous search space.
Experimental Validation: The paper demonstrates the effectiveness of AGE through extensive experiments on various erasure tasks, including object removal, NSFW attribute erasure, and artistic style removal. AGE significantly outperforms state-of-the-art methods in preserving unrelated concepts while effectively erasing undesirable ones.
The authors also introduce the NetFive dataset for evaluating erasure methods and provide metrics for assessing generation capability. Their findings suggest that the concept space is sparse and localized, with the impact of erasing a concept being asymmetric and affecting only semantically related concepts. The paper concludes that AGE offers a superior balance between erasing undesirable concepts and preserving benign ones, supported by a comprehensive study of the concept space's structure.

strengths:
Originality#
The paper presents a novel approach to concept erasure in diffusion models by introducing the Adaptive Guided Erasure (AGE) method. This method departs from the traditional fixed-target strategy by dynamically selecting optimal target concepts, which is a significant innovation in the field. The modeling of the concept space as a graph to understand the localized impact of concept erasure is a creative and original contribution. This approach not only addresses the limitations of existing methods but also provides new insights into the geometric properties of the concept space.
Quality
The quality of the research is high, as evidenced by the thorough empirical analysis and the development of the NetFive dataset for evaluation. The authors provide a comprehensive set of experiments across various tasks, demonstrating the effectiveness of the AGE method. The use of a minimax optimization problem to select target concepts is well-justified and effectively implemented. The paper also includes detailed metrics and comparisons with state-of-the-art methods, which strengthen the validity of the results.
Clarity
The paper is clearly written and well-structured, making it accessible to readers with a background in machine learning and diffusion models. The authors provide a clear explanation of the problem, the limitations of existing methods, and the rationale behind their proposed approach. The use of figures and tables to illustrate the results and the impact of different target concepts enhances the clarity of the presentation. Additionally, the inclusion of appendices with further details and analyses supports the main text and provides a deeper understanding of the methodology.
Significance
The significance of the paper lies in its potential to improve the safety and reliability of diffusion models by effectively erasing undesirable concepts while preserving benign ones. The insights into the concept space's structure and the introduction of the AGE method could inspire future research in concept manipulation and erasure. The paper's contributions are relevant to a wide range of applications, including content moderation, bias reduction, and intellectual property protection in generative models. By addressing a critical limitation of existing methods, this work has the potential to significantly impact the development and deployment of safer AI systems.
In summary, the paper is a strong contribution to the field, offering original insights and a high-quality, well-executed methodology with significant implications for the future of diffusion models and concept erasure.

weaknesses:
arget Concept Selection: The paper could benefit from a more detailed exploration of the target concept selection process, including specific examples and potential challenges.
Scalability: The scalability of the minimax optimization approach for large concept spaces is not fully addressed. Discussing computational complexity and optimization strategies would be helpful.
Generalization: The method's applicability to different types of diffusion or generative models is not thoroughly explored. Additional experiments or discussions on this aspect could enhance the paper's impact.
Evaluation Metrics: A more comprehensive discussion on the choice and limitations of the evaluation metrics used would strengthen the validation of the method's effectiveness.

questions:
How does the method ensure that the erasure of a concept does not inadvertently affect semantically related but benign concepts?
Have you tested the AGE method on other types of diffusion models or generative models? If so, what were the results, and if not, what are the anticipated challenges?
</content>
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</thread>
<thread>
creation date:
1732565776118

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Global response

comment:
Dear the reviewers, 

We sincerely thank you for your time and effort in reviewing our paper and for providing constructive comments. We appreciate your positive feedback on our work and your acknowledgements on the strengths and contributions made in our paper, which we humbly reiterate below for reference.

Reviewer TuUt: " This method departs from the traditional fixed-target strategy by dynamically selecting optimal target concepts, which is a significant innovation in the field. The modeling of the concept space as a graph to understand the localized impact of concept erasure is a creative and original contribution.... In summary, the paper is a strong contribution to the field, offering original insights and a high-quality, well-executed methodology with significant implications for the future of diffusion models and concept erasure"

Reviewer eC2n: "Clever and intuitive knowledge graph-based approach, Clear and well motivated storyline for the proposed objective, with wide variety of empirical experiments"

We note that the reviewers' raised a shared concern regarding the scalability of our method. 
We have addressed this in detail in our rebuttal. In summary, we provided a computational complexity analysis to demonstrate that the optimization problem depends on the batch size (i.e., the number of concepts called per iteration) and the size of the subset of concepts $\mathcal{C}$ rather than the total number of concepts. Furthermore, our implementation employs a dictionary structure to store concept embeddings, enabling efficient updates to target concept embeddings during optimization. To further validate the scalability of our method, we have provided an additional experiment on larger erasure tasks which shows that our method is capable of handling large-scale erasure tasks. All the additional experiments have been uploaded to the anonymous Github repository for reference. 

For other concerns raised, we have provided detailed responses in the corresponding sections of the rebuttal. We believe these address all points comprehensively.

If there are any remaining questions or further clarifications needed, we would be delighted to discuss them.

Thank you once again for your constructive feedback and thoughtful review.

The authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper proposes AGE - a minimax optimization-based dynamical selection technique for mapping undesirable concepts to target concepts while preserving benign (i.e., desirable) concepts. AGE models the concept space as a graph. It shows that: (a) the effect of concept erasure is localized, and (b) at a local neighborhood level synonymous concepts are not optimal as target erasure. 

I agree with majority reviewers that AGE is an original and creative approach toward concept erasure. The motivation is clear and can be agreed upon. Comprehensive experiments have been done to establish the robustness of AGE (along with validation across SOTA generative tasks and architectures). The paper has received credit in terms of clarity. 

Certain concerns that if addressed can make the paper stronger:
1. Scalability issues with large concept spaces and computational demands.
2. Dependence on the accuracy of the concept graph and lack of discussion on potential inaccuracies.
3. Limited exploration of generalization to other diffusion or generative models.
4. Insufficient clarity in presenting results and explaining performance gains or shortcomings.
5. Need for more robust evaluation metrics and human evaluations.