标题： ICAS：从自回归图像生成模型中检测训练数据 显示英文标题

标题： ICAS: Detecting Training Data from Autoregressive Image Generative Models

摘要： 自回归图像生成已经取得了快速的发展，其中具有代表性的模型如逐尺度视觉自回归推动了视觉合成的边界。 然而，这些发展也引发了关于数据隐私和版权的重要担忧。 作为回应，训练数据检测已成为识别模型训练中未经授权数据使用的关键任务。 为了更好地了解自回归图像生成模型对此类检测的脆弱性，我们进行了首次将成员推断应用于该领域的研究。 我们的方法包括两个关键组件：隐式分类和自适应得分聚合策略。 首先，我们在查询图像中计算隐式的逐标记分类得分。 然后我们提出一种自适应得分聚合策略以获得最终得分，该策略更重视得分较低的标记。 较高的最终得分表明该样本更可能涉及训练集。 为了验证我们方法的有效性，我们将原本为大型语言模型设计的现有检测算法适应到视觉自回归模型中。 大量实验表明，我们的方法在类别条件和文本到图像场景中都表现出优越性。 此外，在各种数据变换下，我们的方法表现出强大的鲁棒性和泛化能力。 此外，充分的实验表明了两个新的关键发现：(1) 成员推断的线性缩放定律，揭示了大型基础模型的脆弱性。 (2) 逐尺度视觉自回归模型的训练数据比其他自回归范式更容易检测。 我们的代码可在 https://github.com/Chrisqcwx/ImageAR-MIA 获取。 显示英文摘要

摘要： Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. However, these developments also raise significant concerns regarding data privacy and copyright. In response, training data detection has emerged as a critical task for identifying unauthorized data usage in model training. To better understand the vulnerability of autoregressive image generative models to such detection, we conduct the first study applying membership inference to this domain. Our approach comprises two key components: implicit classification and an adaptive score aggregation strategy. First, we compute the implicit token-wise classification score within the query image. Then we propose an adaptive score aggregation strategy to acquire a final score, which places greater emphasis on the tokens with lower scores. A higher final score indicates that the sample is more likely to be involved in the training set. To validate the effectiveness of our method, we adapt existing detection algorithms originally designed for LLMs to visual autoregressive models. Extensive experiments demonstrate the superiority of our method in both class-conditional and text-to-image scenarios. Moreover, our approach exhibits strong robustness and generalization under various data transformations. Furthermore, sufficient experiments suggest two novel key findings: (1) A linear scaling law on membership inference, exposing the vulnerability of large foundation models. (2) Training data from scale-wise visual autoregressive models is easier to detect than other autoregressive paradigms.Our code is available at https://github.com/Chrisqcwx/ImageAR-MIA.