# CNN Based Image Classification of Malicious UAVs
## 1 Introduction  
The prevalence of Unmanned Aerial Vehicles (UAVs) or drones used for such applications as delivery of goods, remote sensing, surveying, inspection, and recreation has been increasing over the past decade [1]. Like most technologies, UAVs can be misused. The motivation for such malicious use may be to cause annoyance, invade privacy, cause physical harm or even to shutdown airspace with its associated economic impact [2; 3]. Once a malicious UAV is detected, there are various countermeasures that can be considered, including capturing or destroying the UAV, jamming its wireless link so it cannot be controlled or report data back, and identifying and fining the owner [4]. Depending upon the exact scenario, the detection may depend upon a person reporting the malicious UAV to authorities, perhaps with a photo of the UAV included as part of the report. It would be very useful to be able to automatically predict the manufacturer and specific product identification of the malicious UAV from the photo using a trained image classification model, even if the UAV is relatively far away from the person taking the photo. The research problem this paper addresses is whether it is possible to train a deep learning image classification model to accurately classify images of UAVs in flight in terms of the manufacturer and specific product identification. This is a difficult imageclassification problem because the UAVs are in flight and so may be quite distant from the camera, thereby appearing relatively small in an image. In addition, different types of UAVs appear visually similar, so it is not straightforward to distinguish between them. In this paper, we discuss the acquisition of an image dataset of three popular UAVs in flight:  
* the DJI Mavic 2 Enterprise. * the DJI Mavic Air. * the DJI Phantom 4. The first two of these are visually similar, so much so that a person would be challenged to distinguish between them. Images are taken at various UAV elevations and distances using different zoom levels on the camera. We then train various deep learning image classification models based upon Convolutional Neural Networks (CNNs) using the labelled image dataset. In this paper, we are most interested in Resnet-18 because it is relatively lightweight and has such a good reputation in a large variety of image classification tasks. We compare the performance of Resnet-18 against other popular and high performing models; these are AlexNet, VGG-16, and MobileNet v2 [5]. Rather than start the model training from the beginning, we employ the established technique of transfer learning, in which we start with a pre-trained model for a different image dataset and optimize the parameters for the UAV image dataset [6]. This reduces the time required to train the model and makes robust training possible with a smaller dataset. Research into detecting and possibly identifying UAVs, both non-malicious and malicious, using machine learning techniques has been undertaken by several projects over the past decade as the prevalence of industrial and consumer UAVs has increased [7]. The raw data on which these machine learning techniques are trained can be based on audio, images, video, and radar signatures. In this section, we concentrate on the research related to image classification of malicious UAVs since this is the subject matter of the present paper. In [8], the authors used a vision transformer (ViT) framework to model a dataset comprising 776 images of aeroplanes, helicopters, birds, non-malicious UAVs and malicious UAVs. The distinction between non-malicious and malicious UAVs was made primarily based upon whether the UAV was carrying a payload, which was assumed to be harmful and/or illegal. The model achieved an impressive accuracy of 98.3%. Our paper addresses a different problem, whereby a person reports a UAV as malicious based upon its location or behaviour rather than its visual characteristics or whether it is carrying a load, and the problem is to try to identify the type of UAV from the image provided in the report.