# Source Microphone Identification Using Swin Transformer
## 1 Introduction  
Source identification in digital forensics tries to connect multimedia content to a specific type of device that was used to acquire it. It is based on the idea that every device leaves its own unique trace in the content it captures (fingerprint). To identify the source device, specific characteristics are extracted from the unknown content and compared with a database of known characteristics from various devices. Therefore, forensic source identification is essentially a process of classifying content based on its acquisition characteristics. There are several studies conducted on source identification for different fields, such as source camera identification [1, 2], scanner and printer forensics [3], pen-based digitizer devices [4], and microphone and environment identification [5]. Microphone identification is a technique used in audio forensics to determine the specific type of microphone that was used to record a specific piece of audio evidence. It can be used in investigations to determine the origin of a recording or to identify the source of a recording in cases of fraud or forgery. In order to classify a microphone, audio forensics experts may use a combination of techniques, including spectral analysis, impulse-response analysis, and pattern recognition. The spectral analysis involves analyzing the frequency content of the recording, while impulse-response analysis examines how the microphone responds to different types of sounds. Pattern recognition, on the other hand, involves identifying specific patterns or features in the recording that are unique to certain types of microphones. Deep learning, which is a subfield of machine learning [6], has become one of the key development-drivers in the field of artificial intelligence in recent years because ithas achieved good results in many fields and applications, such as natural language processing [7], speech, language, and emotion recognition [8; 9], map generalization [10], landform recognition [11], IoT security [12], etc. This has encouraged many researchers to use it in digital forensics, where high accuracy is required. Transformer deep learning models, such as the Transformer-XL [13] and the BERT [7], have been developed to efficiently process sequential data. These models use self-attention mechanisms to weigh the importance of different parts of the input sequence, which allows them to effectively handle long-term dependencies. This makes them well-suited for processing audio signals, which are inherently sequential in nature. The Swin Transformer is a deep-learning model for computer vision introduced by [PERSON] et al. [14]. It addresses some of the shortcomings of conventional transformer models, such as their difficulty scaling and computational inefficiency. The Swin Transformer uses a hierarchical architecture and shifts windows to achieve better scalability and efficiency. With its hierarchical structure, the Swin Transformer can capture multi-scale contextual information with remarkable effectiveness, much like a CNN. Moreover, the shifted-window-based self-attention mechanism drastically reduces the computational complexity of the self-attention operation, allowing the model to handle larger input sizes without incurring prohibitively high computational costs. By fusing features across different stages, the model can leverage low-level and high-level features, enhancing its capacity to tackle complex visual tasks. In this paper, we propose a system for identifying microphones based on recorded audio using a Swin Transformer deep neural network [14], in which audio recordings are processed to extract features such as the Mel-scale frequency spectrum. These features are then used as inputs to the transformer model, which would learn to distinguish between different types of microphones based on their characteristic patterns.