  Deep learning algorithms have been known to be vulnerable to adversarial
perturbations in various tasks such as image classification. This problem was
addressed by employing several defense methods for detection and rejection of
particular types of attacks. However, training and manipulating networks
according to particular defense schemes increases computational complexity of
the learning algorithms. In this work, we propose a simple yet effective method
to improve robustness of convolutional neural networks (CNNs) to adversarial
attacks by using data dependent adaptive convolution kernels. To this end, we
propose a new type of HyperNetwork in order to employ statistical properties of
input data and features for computation of statistical adaptive maps. Then, we
filter convolution weights of CNNs with the learned statistical maps to compute
dynamic kernels. Thereby, weights and kernels are collectively optimized for
learning of image classification models robust to adversarial attacks without
employment of additional target detection and rejection algorithms. We
empirically demonstrate that the proposed method enables CNNs to spontaneously
defend against different types of attacks, e.g. attacks generated by Gaussian
noise, fast gradient sign methods (Goodfellow et al., 2014) and a black-box
attack(Narodytska & Kasiviswanathan, 2016).
