# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 1 Introduction
### Background and Advances in Steganography with CNN  
Steganography, a discipline within image processing, focuses on concealing information within digital files, such as images, audio, or videos. Although its origin dates back to ancient practices, such as the tattooing of messages on the scalp in ancient Greece in the 5 th century BC [9], in the digital age, it has evolved into much more sophisticated methods that allow information to be hidden quickly and efficiently [10; 11].  
Unlike cryptography, which protects content through encryption, steganography aims to conceal the existence of the message. This characteristic makes it a helpful tool in contexts where monitoring or detection mechanisms are desired, as it allows confidential data to be transmitted covertly [7; 12; 13]. Currently, its application extends to artistic, activist, and even criminal spheres, which has prompted the development of multiple methods for its detection [14; 15].  
Among the most widely used methods is the LSB technique, which alters the lowest-order bit of each pixel, generating changes imperceptible to the naked eye [16; 17]. However, if the process is not performed correctly, visual artifacts or statistical patterns can be generated that compromise the invisibility of the message.  
To address this issue, detection tools have been developed, initially based on statistical methods such as Rich Models (RM) and Subtractive Residual Models (SRM), which, although useful in certain contexts, have limitations when trained from images with compression, noise, or natural variations [4; 14].  
In response to these limitations, deep learning-based techniques have emerged, which have proven effective in steganalysis tasks due to their ability to identify complex image patterns [18; 19]. In particular, CNNs excel in terms of their hierarchical architecture, which enables the extraction of features at different levels of abstraction, thereby facilitating the detection of subtle differences between images with and without steganography [20; 21].  
Among the most relevant models designed specifically for this task are Xu-Net, Yedroudj-Net, and SRNet, which incorporate preprocessing stages to amplify the LSB residual. Other notable works include that of [PERSON] et al. [22], who implemented transfer learning by pretraining the inputs before fine-tuning the network to optimize detection. Similarly, [PERSON] et al. [23] structured their proposal in three stages, input preparation, pretraining, and binary classification using CNNs, achieving superior results compared to traditional methodologies.  
While these models may present certain biases, they have demonstrated improvements over common algorithms. Other notable contributions include the work of [PERSON] et al. [24], who developed a network with multiple filter sizes (\(3\times 3\), \(6\times 6\), \(12\times 12\), and \(15\times 15\)) to adapt to different resolutions without losing accuracy, and that of [PERSON] [25], who exploredthe modification of the two least significant bits per pixel, a technique that, although more visually risky, can evade some detection algorithms focused only on the first bit.  
Similarly, [PERSON] et al. [26] proposed a dual neural network that applies specialized preprocessing filters to each convolutional branch. This design enhances detection performance by identifying complementary features in parallel; however, utilizing two streams can lead to ambiguity in the outputs, potentially affecting model stability.  
Although progress in detection has been significant, most research focuses solely on confirming the presence of steganography without delving into the content or its semantic validity. In this regard, works such as that of [PERSON] et al. [27] have explored message extraction in various formats, but their experimental tests were limited to BMP images, which limits the generalizability of their conclusions.