Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision. Over the past decade, a number of approaches have been proposed, of which the improved Stacked Generative Adversarial Network (StackGAN-v2) has proven capable of generating high resolution images that reflect the details specified in the input text descriptions. In this paper, we aim to assess the robustness and fault-tolerance capability of the StackGAN-v2 model by introducing variations in the training data. However, due to the working principle of Generative Adversarial Network (GAN), it is difficult to predict the output of the model when the training data are modified. Hence, in this work, we adopt Metamorphic Testing technique to evaluate the robustness of the model with a variety of unexpected training dataset. As such, we first implement StackGAN-v2 algorithm and test the pre-trained model provided by the original authors to establish a ground truth for our experiments. We then identify a metamorphic relation, from which test cases are generated. Further, metamorphic relations were derived successively based on the observations of prior test results. Finally, we synthesise the results from our experiment of all the metamorphic relations and found that StackGAN-v2 algorithm is susceptible to input images with obtrusive objects, even if it overlaps with the main object minimally, which was not reported by the authors and users of StackGAN-v2 model. The proposed metamorphic relations can be applied to other text-to-image synthesis models to not only verify the robustness but also to help researchers understand and interpret the results made by the machine learning models.

中文翻译：

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基于变质检验的堆叠式生成对抗网络的鲁棒性评估

从自然语言合成逼真的图像是计算机视觉中的难题之一。在过去的十年中，已经提出了许多方法，其中改进的堆叠生成对抗网络（StackGAN-v2）已被证明能够生成高分辨率图像，这些图像反映输入文本描述中指定的细节。在本文中，我们旨在通过在训练数据中引入变化来评估StackGAN-v2模型的鲁棒性和容错能力。但是，由于生成对抗网络（GAN）的工作原理，修改训练数据时很难预测模型的输出。因此，在这项工作中，我们采用变质测试技术来评估具有各种意外训练数据集的模型的鲁棒性。因此，我们首先实现StackGAN-v2算法并测试原始作者提供的预训练模型，以为我们的实验建立基础。然后，我们确定一个变质关系，从中生成测试用例。此外，基于先前测试结果的观察结果，相继得出了变形关系。最后，我们综合了所有变质关系实验的结果，发现StackGAN-v2算法即使在与主要对象的重叠最小的情况下，也容易受到带有突出物体的输入图像的影响，这并未被作者和用户报道。 StackGAN-v2模型。