Various eHealth applications based on the Internet of Things (IoT) contain a considerable number of medical images and visual electronic health records, which are transmitted through the Internet everyday. Information forensics thus becomes a critical issue. This paper presents a data hiding algorithm for absolute moment block truncation coding (AMBTC) images, wherein secret data, or the authentication code, can be embedded in images to enhance security. Moreover, in view of the importance of transmission efficiency in IoT, image compression is widely used in Internet-based applications. To cope with this challenge, we present a novel compression method named gradient-based (GB) compression, which is compatible with AMBTC compression. Therefore, after applying the block classification scheme, GB compression and data hiding can be performed jointly for blocks with strong gradient effects, and AMBTC compression and data hiding can be performed jointly for the remaining blocks. From the experimental results, we demonstrate that the proposed method outperforms other state-of-the-art methods.

基于物联网（IoT）的各种eHealth应用程序包含大量的医学图像和可视电子健康记录，它们每天都通过Internet传输。信息取证因此成为一个关键问题。本文提出了一种用于绝对矩块截断编码（AMBTC）图像的数据隐藏算法，其中可以将秘密数据或身份验证代码嵌入图像中以增强安全性。此外，鉴于传输效率在物联网中的重要性，图像压缩已广泛用于基于Internet的应用程序中。为了应对这一挑战，我们提出了一种新颖的压缩方法，称为基于梯度（GB）的压缩，它与AMBTC压缩兼容。因此，在应用块分类方案之后，GB压缩和数据隐藏可以对具有强梯度效果的块一起执行，而AMBTC压缩和数据隐藏可以对其余块一起执行。从实验结果中，我们证明了所提出的方法优于其他最新方法。