REFORGE: Multi-modal Attacks Reveal Vulnerable Concept Unlearning in Image Generation Models
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908894017617920 |
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| author | Zou, Yong Li, Haoran Li, Fanxiao Wei, Shenyang Dong, Yunyun Tang, Li Zhou, Wei Liu, Renyang |
| author_facet | Zou, Yong Li, Haoran Li, Fanxiao Wei, Shenyang Dong, Yunyun Tang, Li Zhou, Wei Liu, Renyang |
| contents | Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generation of offensive content. Image Generation Model Unlearning (IGMU) mitigates these risks by removing harmful concepts without full retraining. Despite growing attention, the robustness under adversarial inputs, particularly image-side threats in black-box settings, remains underexplored. To bridge this gap, we present REFORGE, a black-box red-teaming framework that evaluates IGMU robustness via adversarial image prompts. REFORGE initializes stroke-based images and optimizes perturbations with a cross-attention-guided masking strategy that allocates noise to concept-relevant regions, balancing attack efficacy and visual fidelity. Extensive experiments across representative unlearning tasks and defenses demonstrate that REFORGE significantly improves attack success rate while achieving stronger semantic alignment and higher efficiency than involved baselines. These results expose persistent vulnerabilities in current IGMU methods and highlight the need for robustness-aware unlearning against multi-modal adversarial attacks. Our code is at: https://github.com/Imfatnoily/REFORGE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16576 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | REFORGE: Multi-modal Attacks Reveal Vulnerable Concept Unlearning in Image Generation Models Zou, Yong Li, Haoran Li, Fanxiao Wei, Shenyang Dong, Yunyun Tang, Li Zhou, Wei Liu, Renyang Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Machine Learning Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generation of offensive content. Image Generation Model Unlearning (IGMU) mitigates these risks by removing harmful concepts without full retraining. Despite growing attention, the robustness under adversarial inputs, particularly image-side threats in black-box settings, remains underexplored. To bridge this gap, we present REFORGE, a black-box red-teaming framework that evaluates IGMU robustness via adversarial image prompts. REFORGE initializes stroke-based images and optimizes perturbations with a cross-attention-guided masking strategy that allocates noise to concept-relevant regions, balancing attack efficacy and visual fidelity. Extensive experiments across representative unlearning tasks and defenses demonstrate that REFORGE significantly improves attack success rate while achieving stronger semantic alignment and higher efficiency than involved baselines. These results expose persistent vulnerabilities in current IGMU methods and highlight the need for robustness-aware unlearning against multi-modal adversarial attacks. Our code is at: https://github.com/Imfatnoily/REFORGE. |
| title | REFORGE: Multi-modal Attacks Reveal Vulnerable Concept Unlearning in Image Generation Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2603.16576 |