<p>This article integrates semantic segmentation and steganographic embedding into a deep learning framework for accurate scene perception and data protection. The proposed CF-UPerNet architecture applies high-precision semantic segmentation to drive neural-network-based steganographic embedding. Instead of embedding secret data heuristically, segmentation output finds structurally complicated and perceptually insensitive image areas that are suitable hiding places. CF-UPerNet generates robust segmentation maps for objects of different sizes using UPerNet’s multi-scale contextual aggregation and ConvNeXt’s strong feature representation. As a semantic prior for the steganography module, these segmentation maps allow the neural network to understand where data may be placed without decreasing visual quality or triggering statistical detection. The embedding technique becomes content-aware and adaptable, outperforming semantically blind steganography. Rotational data augmentation during training improves segmentation accuracy and embedding robustness for generalization. Experimental evaluation on the Vaihingen and Potsdam datasets demonstrates that CF-UPerNet achieves notable improvements in segmentation performance—1.82% and 9.3% gains in mIoU, respectively—while simultaneously strengthening data security through steganographic concealment. These results confirm that accurate semantic segmentation directly enhances steganographic effectiveness, as better localization of suitable embedding regions leads to higher imperceptibility and resistance to unauthorized access.</p>