# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 1 Introduction  
In recent years, the development of deep learning and the emergence of deep neural networks have lowered the threshold for voice spoofs. This has led to spoofing attacks against automatic speaker verification, increasing security concerns, because voice spoofing poses a severe threat to basic applications of automatic speaker verification, such as phone unlocking and WeChat authentication. These attacks mainly include imitation (mimicry or twinning), replay (prerecorded audio), text-to-speech (converting text to spoken language), and voice conversion (converting voice from the source speaker to the target speaker) [1, 2]. Synthetic speech attacks, including text-to-speech (TTS) and voice conversion (VC), pose an increasing threat to speaker verification systems owing to the rapid development of speech synthesis technologies [3, 4]. It is essential to develop independent spoof detection modules to counteract the harm of speech attacks on speaker verification systems. The ASVspoof challenge [2, 5, 6] has provided datasets and metrics for anti-spoofing speaker verification research. Most traditional approaches rely on feature engineering, such as the Linear Frequency Cepstral Coefficient (LFCC), Constant Q Cepstral Coefficient (CQCC), and Mel Frequency Cepstral Coefficient (MFCC), which have yielded promising results in voice spoof detection. However, the generalization of these manual features is relatively poor, and it is difficult to cope with the rapid development of deep learning techniques. [PERSON] et al. [7] studied deep learning models for spoof detection and demonstrated that a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) could improve the robustness of the system. [PERSON] et al. [8] used a lightweight convolutional gated RNN architecture to improve the long-term dependence on spoof detection. [PERSON] et al. [9] proposed alightweight CNN system based on feature generation, outperforming other single systems in detecting synthetic attacks. Although these methods have shown excellent results in detecting existing spoof voices, they are difficult to generalize in voice attacks generated by spoof algorithms unavailable in the training set. The reason is that they are implemented assuming that the training and test data have the same or similar distributions. However, new spoofing methods have evolved, and the distribution of the new voice spoofing samples may be quite different from that of the voice spoofing data used for training. This difference causes the previously trained model to be useless for such spoofing samples. To improve the generalizability of the trained spoof detection model, a transfer learning method called domain adaptation can adapt the trained model to new spoofing voices. However, domain adaptation has a limitation when the distributions of the source and target domains have a large gap. We often have adequate data on real voices, which is not significantly affected by time. Therefore, we can use a one-class classification approach [10] to solve the distribution mismatch problem when classifying sounds. Real voices belong to a target class which distribution can be fully trained. In contrast, spoof voices belong to a non-target class which samples do not exist in the training set or are not statistically representative. Moreover, most research has focused only on the authenticity of the voice while ignoring the synthesis algorithms behind it. This study focuses on both voice attack detection and voice spoof algorithm recognition. The real voice is distinguished from the voice generated by the TTS and VC algorithms, and then, the voice that is judged to be the spoof is further identified from the spoof algorithm. Voice spoof algorithm recognition can be considered a multi-classifier. As spoof algorithms are continuously proposed, the classifier is either retrained on an increasingly large training set or fine-tuned. The former solution is infeasible, as the data grows sequentially.