| Summary: | The development of new media technology brings serious security problems to the transmission of secret remote sensing or military images. It is a new and challenging task to study the technology of protecting these secret images. In this paper, based on the powerful spatial feature extraction capability of the convolutional neural network, a novel two-channel deep hiding network (TDHN) is designed by introducing advanced ideas such as skip connection, feature fusion, etc., and the two channels are respectively used to input the cover image and the secret image simultaneously. This network consists of two parts: the hiding network and the extraction network. The sender uses the hiding network to hide a secret image in a common cover image and generates a hybrid image called the hidden image. The receiver uses the extraction network to extract and reconstruct the secret image from the hidden image. Meanwhile, an innovative loss function is constructed by introducing two metrics called MSE and SSIM. Experimental results show that the TDHN optimized by the loss function can generate the hidden image and extracted image in high quality. The SSIM value between the hidden image and the original cover image is up to around 0.99, and the SSIM value between the extracted image and the original secret image is up to around 0.98. Through testing on different datasets, it is verified that the designed and optimized TDHN has excellent generalization capability, and thus it has important theoretical significance and engineering value.
|
|---|
