# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 5 Discussion  
The hybrid CNN-NLP model proposed for detecting LSB steganography in digital images demonstrated strong performance in controlled experimental settings, particularly in scenarios with well-preprocessed grayscale images and known encoding patterns. However, several significant limitations must be addressed before deploying the system in real-world conditions. It is essential to point out that the comparison with models such as Xu-Net, Yedroudj-Net, and SRNet was based on different datasets, as these models were initially trained and evaluated on BOSSbase. While this limits direct equivalence in the performance metrics, the comparison still offers valuable insight into architectural differences and the potential impact of semantic validation. As a future line of research, it would be relevant to retrain the proposed CNN-NLP model using the BOSSbase dataset or similar standardized corpora, to conduct experiments under equivalent conditions and evaluate the generalizability of the model across diverse image sources. One of the primary limitations is the reliance of the model on synthetically generated grayscale PNG datasets under idealized conditions. Although these datasets allow controlled experimentation and initial validation, they do not fully capture the variability and unpredictability of real-world steganographic cases, including JPEG compression distortions, color image variability, adversarial noise, or alternative embedding schemes. This limits the generalizability of the model to more diverse formats and practical forensic applications. Specifically, JPEG images pose a unique challenge, as the lossy compression process tends to eliminate or distort the subtle bit-level patterns targeted by LSB-based steganography detection. Similarly, RGB color images introduce additional complexity due to inter-channel correlations and color-space transformations, requiring a different preprocessing pipeline (e.g., extraction of LSB planes from each RGB channel or transformation to the YCbCr color space). While the current model is optimized for grayscale PNGs to maximize LSB fidelity, adapting it to these other formats is a crucial direction for future work. Another challenge concerns the computational cost of the hybrid architecture. The combination of a deep convolutional network with semantic analysis introduces a heavier inference load, making the system less suitable for deployment on low-power devices or in time-constrained environments. Additionally, while the NLP component significantly improves interpretability and semantic validation, its performance depends on the accurate recovery of the hidden message. Any distortion, truncation, or decoding error can compromise the text evaluation phase, leading to false negatives or overlooked threats. An important limitation of the current semantic validation approach arises when the hidden messages are encrypted using ciphers such as Vigenere or other classical encryption schemes. In such cases, the extracted text lacks natural language structure, and the NLP module would correctly classify it as semantically invalid. While this behavior helps prevent false positives from random bit noise, it also implies that the system may fail to detect encrypted stego-content, resulting in false negatives. This design choice is justified by the specific goal of identifying steganographic messages not only present but also interpretable in the linguistic domain, particularly relevant in forensic contexts where message content matters. Nevertheless, detecting encrypted payloads remains an open challenge. Future work could incorporate statistical text analysis (e.g., entropy estimation, \(n\)-gram frequency analysis, or cryptographic feature extraction) as a preliminary filter before NLP validation. Such enhancements could increase the capability of the system to flag hidden encrypted messages, even if their semantic content cannot be directly interpreted.