CSENMT: A deep image compressed sensing encryption network via multi-color space and texture feature

In recent years, the rapid development of wireless communication technology has provided great convenience for human information transmission. Color images have become an important data dissemination and sharing medium due to their large amount of information, vivid and intuitive content. However, color images are easily attacked by third-party illegal users during transmission over public channels and storage in the cloud, and the large image size leads to occupying too many channel resources. Therefore, a deep image compressed sensing encryption network using multi-color space and texture feature (CSENMT) is proposed. Specifically, a multi-color space sampling network based on sparse matrix is presented to obtain the measurement of plain image, and the measurement is shuffled by an adaptive permutation based on chaotic system and plain image (APCP). On the decryption end, the decryption party inversely scrambles the cipher image to obtain the decrypted measurement, and sends it to a deep reconstruction based on multi-color space and texture information (DRMST) to gain the final decrypted reconstructed image. Herein, DRMST can realize high visual performance by using the difference and inter-correlations of several color spaces. Besides, the proposed texture extraction module can focus on extracting texture features to improve the texture details of the reconstructed image. In addition, APCP achieves a higher scrambling degree compared with channel-by-channel permutation. Experimental results demonstrate the advantages of our algorithm for color images in visual performance, efficiency and security.

Automated summary

The rapid development of wireless communication technology has provided great convenience for information transmission. Color images, as an important medium for data dissemination and sharing, face security and efficiency issues. Therefore, a deep image compressed sensing encryption network is proposed using multi-color space and texture features. This network achieves high visual performance by utilizing multiple color spaces and extracting texture details, and it has a higher scrambling degree and the ability to extract texture features.