论文标题
部分可观测时空混沌系统的无模型预测
The Importance of Image Interpretation: Patterns of Semantic Misclassification in Real-World Adversarial Images
论文作者
论文摘要
创建对抗图像的目的是导致图像分类器产生错误分类。在本文中，我们建议应根据语义不匹配而不是标签不匹配来评估对抗图像，如当前工作所用。换句话说，我们建议，如果将“杯子”的图像分类为“萝卜”，而不是当前系统所假定的，则将被视为对抗性。我们在评估对抗图像中考虑语义错误分类的新颖想法提供了两个好处。首先，这是对使图像对抗性的更现实的概念化，这对于充分理解对抗性图像对安全性和隐私的影响很重要。其次，它可以评估对抗图像向真实世界分类器的可传递性，而无需分类器的标签集在创建图像期间可用。该论文对我们的语义错误分类方法对现实世界图像分类器进行转移攻击进行了评估。这次攻击揭示了在对抗性错误分类的语义中的模式，这些模式无法使用常规标签不匹配来研究。
Adversarial images are created with the intention of causing an image classifier to produce a misclassification. In this paper, we propose that adversarial images should be evaluated based on semantic mismatch, rather than label mismatch, as used in current work. In other words, we propose that an image of a "mug" would be considered adversarial if classified as "turnip", but not as "cup", as current systems would assume. Our novel idea of taking semantic misclassification into account in the evaluation of adversarial images offers two benefits. First, it is a more realistic conceptualization of what makes an image adversarial, which is important in order to fully understand the implications of adversarial images for security and privacy. Second, it makes it possible to evaluate the transferability of adversarial images to a real-world classifier, without requiring the classifier's label set to have been available during the creation of the images. The paper carries out an evaluation of a transfer attack on a real-world image classifier that is made possible by our semantic misclassification approach. The attack reveals patterns in the semantics of adversarial misclassifications that could not be investigated using conventional label mismatch.
