Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images

This paper proposes a hybrid model that combines convolutional neural networks with natural language processing techniques for least significant bit-based steganography detection in grayscale digital images. The proposed approach identifies hidden messages by analyzing subtle alterations in the least significant bits and validates the linguistic coherence of the extracted content using a semantic filter implemented with spaCy. The system is trained and evaluated on datasets ranging from 5000 to 12,500 images per class, consistently using an 80% training and 20% validation partition. As a result, the model achieves a maximum accuracy and precision of 99.96%, outperforming recognized architectures such as Xu-Net, Yedroudj-Net, and SRNet. Unlike traditional methods, the model reduces false positives by discarding statistically suspicious but semantically incoherent outputs, which is essential in forensic contexts.