## 1 Introduction
[25] introduced a method combining K-means clustering and ensemble learning. During training, speech samples are grouped based on feature and embedding rate similarities, and each cluster is handled by an XGBoost classifier to enhance detection performance under varying embedding conditions. Despite the development of several steganalysis approaches [24; 25] for recognizing AMR-based speech streams with unknown embedding rates, there are still several problems. Firstly, many existing steganalysis techniques [24; 25] rely on handcrafted features, which are often limited in their ability to extract complex and intricate patterns from steganographic data. Secondly, the existing steganalysis approaches [24; 25] treat AMR-based speech streams with different embedding rates independently, ignoring the inherent correlations between them. By neglecting the interdependencies among embedding rates, these methods fail to leverage the rich contextual information that could enhance classification accuracy. Therefore, this paper proposes a multi-scale transformer-based framework integrated with multi-task learning [26; 27], combining multi-rate regression with multi-rate classification to achieve enhanced steganalysis performance. By incorporating regression [28], the framework models embedding rates as continuous values, capturing fine-grained variations that complement the classification task and leveraging the inter-dependencies between discrete classes and continuous rates. This joint optimization approach improves the model's ability to generalize across diverse scenarios. Additionally, the hierarchical transformer [29; 30]architecture enables unified multi-scale feature extraction, capturing local, pairwise, and global dependencies within speech streams. This design eliminatesreliance on handcrafted features, instead learning nuanced and robust representations directly from the speech streams. The main contributions of this paper are as follows:  
* **Integrating multi-task learning:** Differing from the existing methods [24, 25], our method utilizes the multi-task learning mechanism to enhance the detection performance by combining classification and regression, where classification distinguishes between cover and steganographic samples, and regression estimates the embedding rate. This joint learning framework improves feature extraction by capturing both discrete and continuous variations in embedding rates, enabling the model to learn more informative representations. The regression task provides additional supervision, reinforcing the classification process and improving the model's ability to detect steganographic samples with varying embedding rates. * **Proposing a multi-scale transformer framework:** Unlike the existing steganalysis methods [24, 25] that focus on the handcrafted features, our method proposes the multi-scale transformer-based framework, which can capture hierarchical dependencies within speech frames while preserving both local and global contextual information. The remaining part of this paper is organized as follows. In Section 2, we provide a brief overview of related work on steganography and steganalysis in speech streams, focusing on AMR-based speech streams. Section 3 begins with an overview of the clustering and ensemble learning algorithms used. Section 4 explains the suggested detection mechanism. Section 5 compares the suggested method to past methods. Ultimately, the final section presents the conclusions.