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. INDEX TERMS Convolutional neural network, steganography technology, two-channel deep hiding network, skip connection, feature fusion.
In meaningful secret image sharing (MSIS), a secret image is divided into n shadows. Each shadow is meaningful and similar to the corresponding cover image. Meaningful shadows can reduce the suspicion of attackers in transmission and facilitate shadow management. Previous MSIS schemes always include pixel expansion, and cross-interference from different shadows may exist when cover images are extremely unnatural images with large black and white blocks. In this article, we propose an MSIS with uniform image quality. A threshold t is set to determine the absolute salient regions. More identical bits are allocated according to saliency values in the absolute saliency region, which can improve image quality. In addition, the new identical bits allocation strategy also adjusts the randomness of the shadow images, generating shadows with uniform image quality and avoiding the cross-interference between different shadows. Experimental results show the effectiveness of our proposed scheme.
Image data play an important role in our daily lives, and scholars have recently leveraged deep learning to design steganography networks to conceal and protect image data. However, the complexity of computation and the running speed have been neglected in their model designs, and steganography security still has much room for improvement. For this purpose, this paper proposes an RDA-based network, which can achieve higher security with lower computation complexity and faster running speed. To improve the hidden image’s quality and ensure that the hidden image and cover image are as similar as possible, a residual dense attention (RDA) module was designed to extract significant information from the cover image, thus assisting in reconstructing the salient target of the hidden image. In addition, we propose an activation removal strategy (ARS) to avoid undermining the fidelity of low-level features and to preserve more of the raw information from the input cover image and the secret image, which significantly boosts the concealing and revealing performance. Furthermore, to enable comprehensive supervision for the concealing and revealing processes, a mixed loss function was designed, which effectively improved the hidden image’s visual quality and enhanced the imperceptibility of secret content. Extensive experiments were conducted to verify the effectiveness and superiority of the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.