Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the leaked visual data of concepts. To overcome the challenge, we propose an efficient method, Single Image Unlearning (SIU), to unlearn the visual recognition of a concept by fine-tuning a single associated image for few steps. SIU consists of two key aspects: (i) Constructing Multifaceted fine-tuning data. We introduce four targets, based on which we construct fine-tuning data for the concepts to be forgotten; (ii) Jointly training loss. To synchronously forget the visual recognition of concepts and preserve the utility of MLLMs, we fine-tune MLLMs through a novel Dual Masked KL-divergence Loss combined with Cross Entropy loss. Alongside our method, we establish MMUBench, a new benchmark for MU in MLLMs and introduce a collection of metrics for its evaluation. Experimental results on MMUBench show that SIU completely surpasses the performance of existing methods. Furthermore, we surprisingly find that SIU can avoid invasive membership inference attacks and jailbreak attacks. To the best of our knowledge, we are the first to explore MU in MLLMs. We will release the code and benchmark in the near future.

翻译：机器遗忘通过移除机器学习模型中编码的个人隐私或敏感信息，赋予个体“被遗忘的权利”。然而，机器遗忘能否有效应用于多模态大语言模型（MLLMs），尤其是在遗忘概念泄露视觉数据的场景中，仍存在不确定性。为应对这一挑战，我们提出一种高效方法——单图像遗忘（SIU），通过微调单张相关图像少量步数来实现对概念视觉识别的遗忘。SIU包含两个关键方面：（i）构建多维度微调数据。我们基于四个目标构建待遗忘概念的微调数据；（ii）联合训练损失。为同步实现概念视觉识别的遗忘并保持MLLMs的实用性，我们通过结合交叉熵损失的新型双重掩码KL散度损失对MLLMs进行微调。除方法外，我们建立了MMUBench——一个针对MLLMs机器遗忘的新基准，并提出一套评估指标。在MMUBench上的实验结果表明，SIU完全超越了现有方法的性能。此外，我们意外发现SIU能够规避侵入式成员推理攻击和越狱攻击。据我们所知，我们是首个探索MLLMs中机器遗忘的研究。我们将在近期发布代码和基准测试集。