Privacy-preserving remote sensing images recognition based on limited visual cryptography

With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation-limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking-to-see feature and simple Boolean decryption operation make VC an ideal solution for privacy-preserving recognition for large-scale remote sensing images in IoT. In this study, the secure and efficient transmission of high-resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine-tuning the pre-trained model from large-scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy-preserving recognition framework maintains high recognition performance while enhancing security.

Automated summary

This study proposes a security scheme suitable for computation-limited devices in IoT, achieving secure and efficient transmission of high-resolution remote sensing images using visual cryptography. The recognition performance of small encryption datasets for remote sensing images is improved by fine-tuning the pre-trained model from large-scale datasets.