A multi-branch feature enhancement-based detection and hierarchical chaotic encryption fusion method for sensitive targets in remote sensing images

Remote sensing images used in military reconnaissance contain a large amount of sensitive information, and any leakage may pose a serious threat to national security. To address the need for high-precision detection of sensitive targets and to mitigate information leakage risks, this study proposes a remote sensing image processing framework that integrates multi-object detection with hierarchical chaotic encryption. Based on the YOLOv7-tiny architecture, a Multi-branch Enhanced Feature Aggregation Block (MEFABlock) is designed, which incorporates multi-scale convolutions and attention mechanisms to effectively enhance feature extraction in complex remote sensing scenes. In addition, a coordinate convolution module is introduced before the detection head to strengthen spatial position modeling, thereby achieving higher detection accuracy while maintaining a lightweight network structure.In the encryption stage, a novel two-dimensional chaotic system is constructed, and an improved hash-based method is proposed to generate the initial parameters of the chaotic system. The Josephus-ring permutation process is further enhanced, and a three-stage diffusion–permutation–diffusion encryption structure is employed. The improved detection network is first used to precisely locate sensitive regions in the image; the regions inside the detected bounding boxes are then locally encrypted and embedded back into the original image to achieve information concealment. Finally, global encryption is performed using the chaotic system to ensure end-to-end data security.Experiments conducted on the publicly available MAR20 military aircraft remote sensing dataset demonstrate that the proposed method improves Precision (P) by 2.4%, Recall (R) by 2.7%, [email protected] by 2.7%, and [email protected]:0.95 by 2.2% compared with the baseline model. The encrypted remote sensing images achieve an information entropy of 7.9997, and meet high security standards in key metrics such as NPCR and UACI. Overall, the proposed encryption framework achieves high target detection accuracy and strong information protection performance, exhibiting robust potential for practical engineering applications.