## 2 Related Works  
Liveness detection methods for fingerprints can be classified as hardware-based or software-based. Hardware-based methods use additional hardware devices to obtain the biological features unique to real fingers, such as sensors that detect skin temperature [2] or bioelectrical signals [3]. However, fingermarks lack these vital features. Software-based methods use machine learning or deep learning algorithms to extract dynamic or static features from fingerprint images [4]. These methods determine the liveness of the images and are suitable for various applications, including real-time fingerprint detection and verification. They are highly compatible, cost-effective, and allow for algorithm updates at any time, without requiring additional hardware equipment. Fingermark liveness detection is better suited to static features rather than dynamic features. This is because dynamic features are easily influenced by factors such as finger pressure and skin condition, which are continuous and uncontrollable physiological changes. It is impractical to extract dynamic information from fingermarks on paper documents. Therefore, it is recommended to focus solely on static features for fingermark liveness detection.  
The ridge texture is the most commonly used static feature for detecting the authenticity of fingermarks. Authentic and fake fingermarks can be distinguished based on differences in continuity, clarity, and ductility. These characteristics provide a strong foundation regarding physical properties for effective liveness detection [5; 6; 7; 8; 9; 10]. It is crucial to recognize that traditional texture-based liveness detection methods heavily depend on expert knowledge of manually labeled features. However, as science and technology advance, new means of forgery emerge, making it increasingly challenging to define the characteristics of liveness in fingermark standards. If the manual features used to define the standard are targeted and avoided, these types of liveness detection methods will inevitably become ineffective.  
Convolutional neural networks [11] have been widely used in computer vision. Researchers have applied deep learning to the field of fingerprint liveness detection, providing better robustness and higher performance than detection methods using manual features. Biometric methods applying deep neural networks can be combined with various score fusion strategies [12] to improve recognition accuracy. MobileNet, proposed in [13], significantly reduces the model parameters compared to VGG-19 [14]. CNN-based cross-layer fusion and multi-model voting strategies were proposed in [15] to achieve a better average performance of the network. The DRN designed in [16] achieved good accuracy based on the benchmark dataset LivDet2017 [17; 18].  
Through testing, it is found that the accuracy of the method originally used for fingerprint liveness detection is not ideal after migrating to signed fingermark liveness detection. Compared with the fingerprint images obtained by a fingerprint capture device, the fingermarks obtained from paper documents through a document scanner have to pass through multiple layers of media, with relatively fuzzy texture details and noise interference, such as text, signatures, and other elements, and the influence of signatures on liveness detection is still not clear. Exploring this issue is the goal of this paper.  
In order to cope with these challenges brought about by signed fingermark spoofing attacks, this paper proposes a network model, SFNet, based on a deep residual network structure and combining a multi-probability label classification strategy, designing and combining a multi-score fusion strategy based on the quality weights of the local blocks of fingermarks and a model fusion strategy based on the theory of evidence, in order to achieve the liveness detection of signed fingermarks and to achieve a higher precision based on the fingermark dataset.