## 1 Introduction  
In forensic labs, handwriting examination is mainly performed by trained experts, and the result is primarily based on their experience and domain knowledge. For instance, in Taiwan, most handwritten signature examinations are performed by forensic document examiners of the Criminal Investigation Bureau (CIB). The examiners usually need to collect several signature samples from the suspect to capture handwriting features within available samples. However, it is hard to say how many signature samples would be \"enough\" for each case, which also depends on the expert's experience and ability.  
It is typically not easy for police officers in first-line law enforcement to get enough reference signature samples for examination. In order to carry out the handwriting examination, forensic experts need to collect more signature samples from the suspect. Although more reference samples can promote reliability, they may not be available in all cases.  
In practice, not all signature verification cases will reach the forensic examination stage. In some typical application scenarios such as industry, business, etc., we need to verify signatures in a quick way. For instance, investigators usually require a quick signature examination to evaluate the possibility or guide their investigation when the case is still under the preliminary investigation step. Another example is the use of commercial signature verification. When considering customer convenience, some companies even keep only a single signature for verification.  
The automatic off-line signature verification solutions can be classified into two categories: handcrafted feature extraction algorithms and deep learning methods [1]. The deep learning methods are especially considered to be the most promising approach for its great capability for image recognition and detection. Although studies of deep learning with small-scale data are getting considerable attention in recent years, most of deep learning methods still need a large number of samples to train their system. In other words, most of the studies still need several (more than one) signature samples to accomplish their training process [1; 2; 3; 4].  
In this paper, we propose an off-line handwritten signature verification method using a single known genuine signature. The most challenging issue is how to train the network system with insufficient features of available samples. To conquer the challenge, we propose two alternative strategies to make our training feasible:  
First, a small amount of samples does not necessarily mean a lack of features, and the number of features also depends on the extraction method. More specifically, there are several local features scattered throughout the entire signature, and these local features play an important role in signature verification. Therefore, we design a system based on an explainable deep learning method [5] and unique local feature extraction approach that focuses on verifying local signature blocks (rather than the whole signature image that commonly used in other research work).  
Second, since we are dealing with a binary classification problem (genuine and forged), we can shift the focus of our system to learn \"what is forged signature.\" We let our system learn a lot of features from forged signatures (of which it is easy for us to obtain a sufficient amount of samples) and make up for the disadvantage of insufficient genuine signature features.  
The remainder of this paper is divided into five sections: Section 2 provides a brief discussion of automatic signature verification methods used in the paper. Section 3 introduces some key concepts and describes our methodologies. Section 4 explains the design of experiments and data collection. Section 5 shows the experiment results, evaluates the performance, and visualizes the CNN decision to make our model more interpretable. Finally, in Section 6, we conclude with the advantages and limitations of our approach and describe our future works.