# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 1 Introduction  
In order to protect the applications and stored data, biometric authentication is currently widely used along with other identification modalities to supervise and control system accessibility [1]. Speaker verification (SV) systems exploit speech modalities to identify the user seeking to gain access to systems or services. Specifically, human voiceprint authentication is performed by comparing the voice of the speaker to previously recorded voiceprints. The growing popularity of voice-activated smart home systems has increased the prominence of automatic speaker verification (ASV) technology as a security measure for such devices. These ASV systems also benefit other services such as phone banking and online payment processing. Nevertheless, serious security concerns constrain the potential of these systems. Indeed, spoofing attacks pose a threat to ASV systems [2]. Both the International Electrotechnical Commission (IEC) and the International Organization for Standardization (ISO) have defined such intrusions as presentation attacks (Pas) [3]. These attacks are conducted by criminals impersonating an authenticated user to attempt to gain access to private information [1], and are performed through the use of speech synthesis (SS), replay attacks, and data voice conversion (VC) techniques [1, 2]. Among these techniques, replay attacks are the most common since they do not require substantial technological knowledge. Furthermore, it is difficult to detect such attacks due to the simplicity of the technique, which consists of collecting voice samples and then replaying them. To block these spoofing attacks, it is necessary to devise antispoofing countermeasures. This approach consists of using a classification system that distinguishes between genuine and spoofed utterances. Classifying voice utterances as genuine or spoofed typically entails both performing suitable feature extraction and applying classification technique on these features. In thisregard, potential audio features have been explored. In particular, Q Cepstral coefficients (CQCC) [4] and the linear frequency Cepstral coefficient (LFCC) [5] have been explored. Alternatively, for the supervised learning model, Gaussian mixture models (GMMs) [6] and deep neural networks (DNNs) [7] have been utilized in the design of spoofing countermeasures. Specifically, several deep learning architectures have been adapted to serve antispoofing purposes, such as residual neural networks (ResNets) [8] and recurrent neural networks (RNNs) [9]. Nevertheless, these models are outperformed by Gaussian mixture model (GMM)-based techniques, which were even more successful than existing industry-leading approaches [2]. Despite these advances, the trained models perform poorly when classifying new instances. This is known as the generalization problem, and it represents a significant challenge for spoofing counter-measures [2]. This problem is mainly due to the large variance of the intra-class instances [10; 11]. For genuine utterances, disparities in the user voice represent the primary reason for this impediment. Indeed, the voice can be distorted by many factors such as emotional state, user health, or the authentication device, in addition to the obvious discrepancy between different users' voiceprints [12; 13]. While the same variation factors apply to spoofed utterances, the latter feature additional forms of variation, such as those caused by recording devices or the algorithms deployed for manipulation purposes [2]. Given the above factors, the generalization problem can be addressed by handling the variance in both genuine and spoofed utterances. In this regard, we propose to tackle the variance problem by dividing the classification problem into several subproblems. This is performed by learning the hidden partitions of the utterances through the use of clustering techniques. Then, a countermeasure suitable for each subgroup is devised. For this purpose, various classifiers are investigated with respect to each subgroup.