While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which targets the learnt features in the hidden layers to improve its transferability across different models. Yet it is observed that the transferability has been largely impacted by the neuron importance estimation results. In this paper, a double adversarial neuron attribution attack method, termed `DANAA', is proposed to obtain more accurate feature importance estimation. In our method, the model outputs are attributed to the middle layer based on an adversarial non-linear path. The goal is to measure the weight of individual neurons and retain the features that are more important towards transferability. We have conducted extensive experiments on the benchmark datasets to demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/Davidjinzb/DANAA

深度神经网络中存在来自攻击样本的干扰，其中特征级攻击是一种有效的攻击类型，本文提出了一种名为'DANAA'的双重对抗神经元归因攻击方法，用于获取更准确的特征重要性估计，并通过基于对抗非线性路径将模型输出归因于中间层，以衡量单个神经元的权重，并保留对于传递性更重要的特征。我们在基准数据集上进行了大量实验，展示了我们的方法的最新性能。

DANAA：具有双重敌对神经元归因的可转移攻击