隐写术
隐写分析技术
计算机科学
人工智能
互联网
隐写工具
判别式
远程医疗
计算机安全
模式识别(心理学)
万维网
图像(数学)
经济增长
医疗保健
经济
作者
Kai Chen,Zhengyuan Zhou,Yuchen Li,Xu Ji,Jiasong Wu,Gouenou Coatrieux,Jean-Louis Coatrieux,Yang Chen
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:28 (3): 1611-1622
标识
DOI:10.1109/jbhi.2023.3316468
摘要
Internet of Medical Things (IoMT) and telemedicine technologies utilize computers, communications, and medical devices to facilitate off-site exchanges between specialists and patients, specialists, and medical staff. If the information communicated in IoMT is illegally steganography, tampered or leaked during transmission and storage, it will directly impact patient privacy or the consultation results with possible serious medical incidents. Steganalysis is of great significance for the identification of medical images transmitted illegally in IoMT and telemedicine. In this article, we propose a Residual and Enhanced Discriminative Network (RED-Net) for image steganalysis in the internet of medical things and telemedicine. RED-Net consists of a steganographic information enhancement module, a deep residual network, and steganographic information discriminative mechanism. Specifically, a steganographic information enhancement module is adopted by the RED-Net to boost the illegal steganographic signal in texturally complex high-dimensional medical image features. A deep residual network is utilized for steganographic feature extraction and compression. A steganographic information discriminative mechanism is employed by the deep residual network to enable it to recalibrate the steganographic features and drop high-frequency features that are mistaken for steganographic information. Experiments conducted on public and private datasets with data hiding payloads ranging from 0.1bpp/bpnzac-0.5bpp/bpnzac in the spatial and JPEG domain led to RED-Net's steganalysis error PE in the range of 0.0732-0.0010 and 0.231-0.026, respectively. In general, qualitative and quantitative results on public and private datasets demonstrate that the RED-Net outperforms 8 state-of-art steganography detectors.
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