State-of-the-art deep neural networks have proven to be highly powerful in a
broad range of tasks, including semantic image segmentation. However, these
networks are vulnerable against adversarial attacks, i.e., non-perceptible
perturbations added to the input image causing incorrect predictions, which is
hazardous in safety-critical applications like automated driving. Adversarial
examples and defense strategies are well studied for the image classification
task, while there has been limited research in the context of semantic
segmentation. First works however show that the segmentation outcome can be
severely distorted by adversarial attacks. In this work, we introduce an
uncertainty-based method for the detection of adversarial attacks in semantic
segmentation. We observe that uncertainty as for example captured by the
entropy of the output distribution behaves differently on clean and perturbed
images using this property to distinguish between the two cases. Our method
works in a light-weight and post-processing manner, i.e., we do not modify the
model or need knowledge of the process used for generating adversarial
examples. In a thorough empirical analysis, we demonstrate the ability of our
approach to detect perturbed images across multiple types of adversarial
attacks.

本研究提出基于不确定性的语义分割对抗攻击检测方法，不需修改模型或了解对抗样本生成过程，通过熵等指标区分干净和攻击图片，在多种对抗攻击类型下检测效果良好。