STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation Models (2408.16807v1)

Abstract: The rapid proliferation of large-scale text-to-image generation (T2IG) models has led to concerns about their potential misuse in generating harmful content. Though many methods have been proposed for erasing undesired concepts from T2IG models, they only provide a false sense of security, as recent works demonstrate that concept-erased models (CEMs) can be easily deceived to generate the erased concept through adversarial attacks. The problem of adversarially robust concept erasing without significant degradation to model utility (ability to generate benign concepts) remains an unresolved challenge, especially in the white-box setting where the adversary has access to the CEM. To address this gap, we propose an approach called STEREO that involves two distinct stages. The first stage searches thoroughly enough for strong and diverse adversarial prompts that can regenerate an erased concept from a CEM, by leveraging robust optimization principles from adversarial training. In the second robustly erase once stage, we introduce an anchor-concept-based compositional objective to robustly erase the target concept at one go, while attempting to minimize the degradation on model utility. By benchmarking the proposed STEREO approach against four state-of-the-art concept erasure methods under three adversarial attacks, we demonstrate its ability to achieve a better robustness vs. utility trade-off. Our code and models are available at https://github.com/koushiksrivats/robust-concept-erasing.

• The paper introduces STEREO, a two-stage framework that robustly erases undesired concepts from text-to-image models even under adversarial conditions.
• It employs a dual-phase approach with STE for adversarial prompt discovery and REO for anchor-guided erasure that balances robustness and generative performance.
• Empirical results show a significant reduction in adversarial attack success and improved preservation of benign content compared to prior methods.

An Analytical Overview of STEREO: Enhancing Adversarial Robustness in Concept Erasure for Text-to-Image Generation Models

The paper "STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation Models" proposes a novel method for robustly erasing undesired concepts, such as nudity and specific artistic styles, from text-to-image generation models. This methodological advancement is particularly relevant given the increasing capability of these models to produce high-fidelity images from textual descriptions, coupled with growing concerns about the generation of harmful or inappropriate content.

The core contribution of the paper is the introduction of STEREO, a two-stage approach designed to fortify concept erasure against adversarial attacks while maintaining the model’s ability to generate benign concepts proficiently. In the first stage, termed "Search Thoroughly Enough" (STE), the method rigorously searches for adversarial prompts that could potentially regenerate erased concepts. The second stage, "Robustly Erase Once" (REO), then employs an anchor-concept-based compositional objective to securely erase the target concept while regularly balancing robustness and utility.

Technical Contributions and Methodology

The proposed STEREO framework addresses the inadequacy of prior concept erasure methods in handling adversarial attacks by introducing a min-max optimization schema. The first phase, STE, tackles the iterative min-max problem by finding adversarial prompts that maximize the chance of regenerating the erased concept. To achieve this, it uses robust optimization principles from adversarial training to create and fine-tune adversarial prompts, ensuring that these prompts effectively challenge the robustness of the concept-erased model.

The REO stage addresses utility preservation and robust concept erasure simultaneously via a novel composition of adversarial prompts derived from STE. This stage leverages positive guidance towards an anchor concept which helps maintain the generative quality of non-target concepts. The compositional objective here ensures that erasing the target concept does not lead to excessive degradation in the model's performance on benign concepts.

Assessment and Implications

Empirical results substantiate the efficacy of STEREO over existing methods, particularly in high-adversarial settings. For example, STEREO exhibited a substantial reduction in the attack success rates of robust adversarial (RAB) and circumventing concept erasure (CCE) attacks, while achieving negligible or zero attack success on benign concept retrieval, a substantial advance over existing benchmarks. The trade-off between robust concept erasure and utility preservation is significantly improved, showcasing a balanced approach to handling such dual objectives in generative models.

In terms of practical implications, STEREO's development is a step forward in enhancing secure deployment of text-to-image models in various applications, ranging from content moderation to artistic software tools, where inadvertent or malicious creation of inappropriate content poses significant ethical and legal challenges. Theoretically, this work opens up new directions in exploring robust optimization techniques in generative model frameworks, potentially influencing future research in adversarial resilience and explainability in AI systems.

Future Directions

The paper concludes with a broader vision for future explorations including extending the proposed method for multi-concept erasure and lowering the computational time required for adversarial prompt discovery. Additionally, given the complexity of adversarial spaces, leveraging automated approaches to discover broader sets of adversarial prompts might serve as a fruitful research avenue. Furthermore, the integration of domain-specific knowledge into the anchor prompts could refine the precision of guidance used in erasure, enhancing applicability across more diverse domains.

In summary, the introduction of STEREO represents a significant progression in concept erasure methodologies, providing an insightful framework for addressing the dual challenges of robustness and utility in text-to-image generative models.