Improving The Robustness Of Convolutional Neural Networks Via Sketch Attention

The convolutional neural networks (CNNs) is biased towards texture while human eyes relying heavily on the general structure. The inconformity leads to the vulnerability of CNNs. The convolutional results is determined by the local patterns and delicate adversarial perturbation would be amplified layer-wise. Meanwhile the image context and object structure, which can be represented by sketch, stay almost unchanged. Therefore we propose that the sketch information is weak but more robust. In order to transfer the robustness from sketch to image and improve the capture of global structure, a sketch attention guided CNNs (SAG-CNNs) pipeline is constructed. The experiments on CIFAR-10 demonstrate that the defensive capability against black-box attacks of SAG-CNNs outperforms other counterparts evidently and achieve preferable trade-off between generalization and robustness.