Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN's robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., largely relying on the shape features, this paper first employs the edge detectors as layer kernels and designs a binary edge feature branch (BEFB for short) to learn the binary edge features, which can be easily integrated into any popular backbone. The four edge detectors can learn the horizontal, vertical, positive diagonal, and negative diagonal edge features, respectively, and the branch is stacked by multiple Sobel layers (using edge detectors as kernels) and one threshold layer. The binary edge features learned by the branch, concatenated with the texture features learned by the backbone, are fed into the fully connected layers for classification. We integrate the proposed branch into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate the BEFB is lightweight and has no side effects on training. And the accuracy of the BEFB integrated models is better than the original ones on all datasets when facing FGSM, PGD, and C\&W attacks. Besides, BEFB integrated models equipped with the robustness enhancing techniques can achieve better classification accuracy compared to the original models. The work in this paper for the first time shows it is feasible to enhance the robustness of DCNNs through combining both shape-like features and texture features.

翻译：深度卷积神经网络（简称DCNN）易受微小扰动的样本影响。提升DCNN的鲁棒性对于自动驾驶和工业自动化等安全关键应用具有重要意义。受人类视觉系统主要依赖形状特征识别物体的核心机制启发，本文首次将边缘检测器用作卷积层核，设计了一个二值边缘特征分支（简称BEFB）来学习二值边缘特征，该分支可便捷地集成到任意主流骨干网络中。四个边缘检测器分别学习水平、垂直、正对角线和负对角线的边缘特征，分支由多个Sobel层（以边缘检测器为卷积核）和一个阈值层堆叠而成。分支学习到的二值边缘特征与骨干网络学习到的纹理特征进行拼接后，输入全连接层进行分类。我们将所提分支分别集成到VGG16和ResNet34中，并在多个数据集上开展实验。实验结果表明BEFB具有轻量级特性且对训练无副作用。在面对FGSM、PGD和C&W攻击时，集成BEFB的模型在所有数据集上的准确率均优于原始模型。此外，配备鲁棒性增强技术的BEFB集成模型相比原始模型能实现更优的分类准确率。本文工作首次证明，通过融合类形状特征与纹理特征来增强DCNN的鲁棒性是可行的。