## 1 Introduction  
Over the years, people have obtained greater access to communication devices and networks, resulting in more information sent over the internet, such as text messages, audio, images, and videos. Since these types of information can be changed and eavesdropped upon, encryption and data hiding techniques, such as steganography and watermarking, can be applied to achieve both malicious and good objectives [1]. Steganography is a subarea of data hiding with the objective of sending an occult message through a communication channel [2]. Steganalysis is a counter mechanism that focuses on discovering whether there is the presence of hidden information, thus counteracting steganography [3]. In the digital era, steganography methods have been used for malicious objectives, e.g., to leak information [4, 5], for terrorism purposes [6, 7], for cyber warfare [8], and for malware propagation [9, 10, 11]. As a countermeasure, steganalysis schemes have been employed to track criminal activities on the internet [12, 13], to combat cyber warfare [14], in the detection of malware [15, 16], and in forensics [13, 17, 18]. Steganography methods can be classified via the domain used to embed secret information. While spatial domain techniques consist of hiding information by changing the image pixel values, transform domain methods are based on embedding messages in the transform coefficients of images [19]. Among the steganography algorithms adopted to evaluate the performance of steganalysis in the spatial domain are Highly Undetectable steGO (HUGO) [20], wavelet-obtained weights (WOW) [21], spatial universal wavelet relative (S-UNIWARD) [22], and high-pass, low-pass, and low-pass (HILL) [23]. Steganalysis methods can be classified into specific steganalysis, which means that the technique is responsible for discovering whether there is information concealed in a specific steganography method, or universal/black-box/blind steganalysis, which can detect the existence of information embedded by any steganographic scheme, mostly through computational intelligence techniques [24]. These methods are used to discover if there is hidden communication in the most popular types of media [25], such as audio [26], video [27], text [28], and images [29]. In recent years, deep learning models for image steganalysis have been developed to improve the detection of hidden messages in images [30; 31; 32]. They have been proposed as an alternative to traditional methods, seeking to achieve better performance in terms of accuracy in detecting the presence of steganography. Different scenarios have been evaluated by changing the payload capacity of the chosen steganographic algorithm and the arrangement of the test, validation, and train image sets [33]. [PERSON] et al. [34] proposed a deep learning network applied to image steganalysis called GBRAS-Net, which achieves competitive results in terms of accuracy and belongs to the group of models that require less memory when compared to previously proposed models. Deep learning models can have a structure with millions of parameters and many activation functions to be stored, resulting in concerns regarding the implementation of these models in resource-limited devices. To overcome this issue, efficient and smaller networks have been proposed recently by applying optimization techniques such as model pruning, quantization, and weight clustering, without a significant reduction in terms of accuracy [35; 36; 37]. This work aims to assess the effects of deep learning model compression strategies, namely quantization, pruning, and weight clustering, in a deep learning model applied to image steganalysis (GBRAS-Net). An evaluation in terms of accuracy and memory requirements is carried out for a wide variety of compression strategies. The main scientific contribution of this research lies in the implementation of optimization strategies in a deep learning model for image steganalysis, specifically through the application of pruning, quantization, and weight clustering.