Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication of secret images in the context. Stego images thus generated are subjected to a deep learning approach to assess if it can be classified as a cover or not. If not, the HS parameter is modified to generate stego images in such a way to classify it as a cover image. Thus it is difficult to suspect the existence of a secret image by the Human Visual System (HVS). The efficiency of our method is analyzed by comparing it with related methods present in the literature. Average NCC values between the original secret image and the extracted secret image are 0.8917 which is higher than the schemes in the literature. Average PSNR values of the stego image are 36.375 even when the embedding rate is increased to 4 bits per pixel. The analysis was done on security and robustness also reveals better results. From the experimental analysis, it is proved that the proposed method is superior to the related methods of the literature.

中文翻译：

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基于 RIWT-Laplacian 金字塔和使用深度学习的直方图移动的鲁棒图像隐写方法

如今，高度敏感的医学图像容易受到数据威胁和隐私攻击。正是为此目的，在通过不安全的通道传输它们时，它们必须保持安全。通过利用冗余整数小波变换 (RIWT)、拉普拉斯金字塔、阿诺德加扰和直方图移位算法来促进上下文中秘密图像的安全通信，稳健的图像隐写术专注于这项工作。这样生成的 Stego 图像经过深度学习方法来评估它是否可以归类为封面。如果不是，则修改 HS 参数以生成隐写图像，将其分类为封面图像。因此很难通过人类视觉系统（HVS）来怀疑秘密图像的存在。通过将其与文献中存在的相关方法进行比较来分析我们方法的效率。原始秘密图像和提取的秘密图像之间的平均 NCC 值为 0.8917，高于文献中的方案。即使嵌入率增加到每像素 4 位，隐写图像的平均 PSNR 值为 36.375。对安全性和稳健性进行的分析也显示出更好的结果。实验分析表明，该方法优于文献中的相关方法。对安全性和稳健性进行的分析也显示出更好的结果。实验分析表明，该方法优于文献中的相关方法。对安全性和稳健性进行的分析也显示出更好的结果。实验分析表明，该方法优于文献中的相关方法。