  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.
