DenseJIN: Dense Depth Image Steganography Model With Joint Invertible and Noninvertible Mechanisms
Delin Duan, Shuyuan Shen, Songsen Yu, Yibo Yuan, Qidong Zhou, Haojie Lv, Huanjie Lin · IEEE Transactions on Circuits and Systems for Video Technology · 2024
Image steganography discreetly embeds secret information within a carrier, allowing covert communication and enabling the receiver to extract the concealed data when needed. Previous techniques for image steganography had limitations in achieving imperceptibility and security when dealing with images containing intricate textures. In this paper, we introduce DenseJIN, an innovative model for dense depth image steganography. DenseJIN joins invertible and noninvertible mechanisms to achieve effective and secure information hiding. The invertible component of DenseJIN ensures that the stego image maintains high imperceptibility and security, while the noninvertible component enables high-quality recovery of the secret image. In the invertible component, we employ a dense connection for each invertible block in the forward process and a straightforward series connection during the reverse process. In the forward process of the network, the secret image is embedded, while the backward process is responsible for extracting the embedded secret image. To perform the noninvertible step, we incorporate a modified Unet architecture, enabling deep fine-grained feature extraction from cover images and secret images. Our experimental results indicate that DenseJIN surpasses other contemporary image steganography methods. On average, DenseJIN achieves a remarkable improvement of over 1.75 dB in PSNR for secret image recovery across DIV2K, COCO and ImageNet.