Existing adversarial example detection methods based on image transformation employ the characteristic that the image transformation can significantly change the feature distribution of adversarial examples but slightly change the feature distribution of benign examples. Adversarial examples can be detected by calculating the feature distance before and after image transformation. However, with the deepening research on adversarial attacks, researchers pay more attention to enhancing the robustness of adversarial examples, so that some attacks can be “immune” to the effect exerted by image transformation. Existing methods are difficult to detect robust adversarial examples effectively. This paper observes that the existing adversarial examples are too robust, and the feature distribution distance of robust adversarial examples under image transformation is much smaller than that of benign examples, which is not consistent with the feature distribution laws of benign examples. Based on this key observation, this study proposes a dual-threshold adversarial example detection based on image transformation, which sets a lower threshold combining existing single-threshold methods to form a dual-threshold detection interval. An example whose feature distribution is not within the dual-threshold detection interval will be judged as an adversarial example. Additionally, this study conducts extensive experiments on VGG19, DenseNet, and ConvNeXt models for image classification. The results show that the proposed approach is compatible with the detection ability of existing single-threshold detection schemes, and yields outstanding detection performance against robust adversarial examples.