Model Parsing defines the research task of predicting hyperparameters of the generative model (GM), given a generated image as input. Since a diverse set of hyperparameters is jointly employed by the generative model, and dependencies often exist among them, it is crucial to learn these hyperparameter dependencies for the improved model parsing performance. To explore such important dependencies, we propose a novel model parsing method called Learnable Graph Pooling Network (LGPN). Specifically, we transform model parsing into a graph node classification task, using graph nodes and edges to represent hyperparameters and their dependencies, respectively. Furthermore, LGPN incorporates a learnable pooling-unpooling mechanism tailored to model parsing, which adaptively learns hyperparameter dependencies of GMs used to generate the input image. We also extend our proposed method to CNN-generated image detection and coordinate attacks detection. Empirically, we achieve state-of-the-art results in model parsing and its extended applications, showing the effectiveness of our method. Our source code are available.

翻译：模型解析定义了这样一个研究任务：给定生成的图像作为输入，预测生成模型的超参数。由于生成模型联合使用多样化的超参数集，且超参数之间往往存在依赖关系，学习这些超参数依赖关系对于提升模型解析性能至关重要。为探索此类重要依赖关系，我们提出了一种名为可学习图池化网络的新型模型解析方法。具体而言，我们将模型解析转化为图节点分类任务，用图节点和边分别表示超参数及其依赖关系。此外，LGPN引入了专门为模型解析设计的可学习池化-解池化机制，能够自适应学习用于生成输入图像的生成模型的超参数依赖关系。我们将所提方法进一步拓展至卷积神经网络生成图像检测与协同攻击检测。实验结果表明，我们在模型解析及其扩展应用中取得了最先进的效果，验证了方法的有效性。我们的源代码已公开。