Open AccessHCISNet: Higher‐capacity invisible image steganographic network
Author(s) -
Li Yafeng,
Liu Ju,
Liu Xiaoxi,
Wang Xuejing,
Gao Xuesong,
Zhang Yuyi
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12329
Subject(s) - steganography , computer science , discriminator , information hiding , steganalysis , robustness (evolution) , artificial intelligence , block (permutation group theory) , image (mathematics) , pattern recognition (psychology) , computer vision , mathematics , telecommunications , biochemistry , chemistry , geometry , detector , gene
Abstract Information hiding as a crucial method for multimedia security and privacy protection, has received a substantial amount of attention. However, the most existing methods focus on the resistance of steganalysis and the robustness of information, resulting in low capacity. In this paper, to further increase the capacity of information hiding, a high‐capacity adversarial image steganography model with end‐to‐end manner, termed as HCISNet is proposed. First, an enhanced Dense Atrous Spatial Pyramid Pooling module is presented to learn more semantic information in cover image, which can adaptively embed more message in redundant regions with rich textures. Then, a modified discriminator network with lightweight residual block is employed to assist the encoder network with optimal high‐capacity solution. Meanwhile, the multiple objective function with the perceptual loss and several training tricks are developed, which can improve the visual and perceptual consistency of the original and the steganographic image, further enhancing the capacity. Finally, experiments and analysis are conducted on three public datasets, where this model has better imperceptibility, higher security and greater capacity. Under same experimental conditions, the cover image can embed 5.68 bits per pixel (BPP), far exceeding the previous highest value of 4.4 BPP in state‐of‐the‐art methods in the authors' knowledge.