In Computer Vision (CV) context, the image classification is a supervised learning problem which has several applications in all fields such as autonomous driving, medical diagnosing, remote sensing and so on. In the Deep Learning (DL) field, the image classification problem is solved by several architectures: from the simple Convolutional Neural Networks (CNNs) to the more complex models such as Capsule Networks (CapsNets). An important aspect of image classification is the robustness of the architectures against adversarial attacks: an image can be misclassified crafting a small perturbation to the input. In this dissertation, the adversarial attacks are crafted on Residual Neural Networks (ResNet) and CapsNet models.