## 1 Introduction  
Recent advances in Software-Defined Radios (SDR) and cognitive networking technologies, as well as increasing the accessible low-cost hardware, have led to most applications becoming dependent on the wireless networks [1]. It provides adversaries with an opportunity to deploy the jamming attacks (also known as the intentional Radio Frequency Interference--RFI) and harm systems that rely on wireless networks [1]. Jamming attacks cause Denial-of-Service (DoS) problems such as slowing browsing websites and downloading files, intensively limiting the number of active voice users, and as a result, network latency [2]. Although the jammers can be launched using simple and cheap technologies, they are hard to defeat due to the large variety of available jammers completely [3]. To guarantee the Quality of Service (QoS) and security of the wireless communication system, a robust RFI detection strategy is required to produce an effective mitigation process [3]. Besides, it is essential to precisely determine the modulation type of SoI combined by any type of RFI. Since Automatic Modulation Classification (AMC) is a significant procedure in communication networks to facilitate the demodulation process at the receiver side [4]. To address this concern, Machine Learning (ML) based techniques have shown promising results in the area of multiclass RFI recognition [5; 6] and Automatic modulation classification (AMC) [6]. However, the complex nature of preprocessing tasks such as feature extraction and feature selection in classical ML techniques highly degrades the classification performance regarding efficiency [7]. To tackle this issue, deep learning (DL)-based approaches, as a subfield of ML, have presented outstanding RFI detection results. DL-based techniques include numerous information processing layers in a hierarchical design for either pattern classification or feature extraction [8]. One of the most successful types of DL is Convolutional Neural Networks (CNN), which has been typically used for object detection in computer vision fields, without any prior knowledge regarding the object's location [9]. The main challenge of DL in supervised learning applications could be the lack of enough data to train the model from scratch. To address this issue, the image-based transfer learning method has gained attraction in the case of an insufficient dataset to create models [10; 11]. Transfer learning refers to reuse the pretrained CNN architectures on a prebuild large dataset, such as the ImageNet project [10]. Hence, transfer learning leads to minimizing the training time by considering the pretrained layers of a model [10]. The main contribution of this work is not only specifying the type of received signals but also determining different schemes of digital modulation in the presence of jamming signals in a real-time digital video broadcasting based on DVB-S2 standard using transfer learning. To this end, we propose a hierarchical classification design for RFI classification and AMC by leveraging the benefits of transfer learning technology using pretrained CNNs such as AlexNet, VGG16, GoogleNet, and ResNet18 for feature learning, followed by a fully-connected classifier. This study provides a comparative analysis of these pretrained CNNs with respect to accuracy in the context of transfer learning and consumed training time. We have generated visual representations of the received signals in the time-frequency domain as the input data, which is the magnitude squared of the wavelet transform known as scalogram [12]. In this work, SoI is a video stream transmitted in a digital video broadcasting scenario based on DVB-S2 standard in a real-time Satellite communication (Satcom). We have assumed that SoI is combined with three well-known types of jammers, namely, continuous-wave interference (CWI), multi-CWI (MCWI), and chirp interference (CI), to increase the scenarios complexity and to simulate the realistic situations [5].