Convolutional architectures are commonly used in practice and the popularity of Cellular Neural Networks has also increased significantly in the past years, since these architectures can provide an efficient, low-energy, analogue implementation of deep neural networks. Both architectures were applied with success in various tasks, but in safety critical applications, like medical imaging or self-driving cars the high accuracy of these approaches is not enough, adversarial attacks still pose a significant threat in these areas. In this paper I will examine and compare how resilient these structures are towards various adversarial attacks and I will also investigate possible causes of these differences.