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.

翻译：近年来，图像生成模型（IGMs）的进展使得高保真内容创作成为可能，但也放大了相关风险，包括受版权保护内容的复制和冒犯性内容的生成。图像生成模型遗忘（IGMU）通过移除有害概念而无需完全重新训练，以缓解这些风险。尽管受到越来越多的关注，其在对抗性输入下的鲁棒性，特别是在黑盒设置下面临的图像侧威胁，仍未得到充分探索。为填补这一空白，我们提出了REFORGE，一个黑盒红队测试框架，通过对抗性图像提示来评估IGMU的鲁棒性。REFORGE初始化基于笔触的图像，并采用交叉注意力引导的掩码策略优化扰动，该策略将噪声分配到概念相关区域，从而平衡攻击效果与视觉保真度。在代表性遗忘任务和防御方法上进行的大量实验表明，与涉及的基线方法相比，REFORGE显著提高了攻击成功率，同时实现了更强的语义对齐和更高的效率。这些结果揭示了当前IGMU方法中持续存在的脆弱性，并强调了针对多模态对抗攻击开发具备鲁棒性意识的遗忘方法的必要性。我们的代码位于：https://github.com/Imfatnoily/REFORGE。