医学图像的多隐藏器秘密共享可逆信息隐藏

Reversible Information Hiding Based on Secret Sharing of Multiple Hiding Devices in Medical images

云存储等技术在给人们生活带来便利的同时,也给隐私安全带来了巨大隐患。可逆信息隐藏技术广泛应用于保护用户云服务器端数据安全。针对云环境中医学图像易遭受攻击的问题,提出一种基于秘密共享和多隐藏器的对比度增强的医学图像可逆信息隐藏算法。该算法首先通过对图像进行直方图拉伸实现对比度增强,提高了嵌入容量和图像质量;然后利用不同的直方图嵌入方法将隐私数据分别隐藏到医学图像ROI/RONI区域中;最后使用秘密共享技术将图像加密成与其大小相同的多个不同加密图像,并将它们分别发送给多个不同数据隐藏器进行隐藏,即使某些数据隐藏器受到攻击或发生背叛,也可以从剩余数据隐藏器中收集足够数量的标记加密图像来恢复原始图像。实验结果表明,与相关算法相比,所提算法嵌入率最高提升了568.56%,最低也提高了33.39%;加密过后的Share图像相关性低至0.0012;含密图像的RCE、PSNR、SSIM和NC的平均值分别高达0.5245、30.08、0.9491和0.9996。与相关算法相比,该算法在综合性能方面有明显优势。

Cloud storage and other technologies not only bring convenience,but also bring huge risks in terms of security.Reversible information hiding technology has attracted more and more attention from researchers and has been widely used to protect the privacy of users′cloud server data.Aiming at the problem that medical images are vulnerable to attack,a reversible medical image information hiding algorithm based on secret sharing and contrast enhancement of multiple hiders is proposed.Firstly,histogram stretching is used to enhance the contrast and improve the embedding capacity and image quality.Then,different histogram embedding methods are used to hide the private data in the ROI/RONI region of the medical image.Finally,the secret sharing technology is used to encrypt the image into a number of different encrypted images of the same size,and then send them to a number of different data hiders for data hiding.Even if some data hiders are attacked or betrayed,the original image can be recovered by collecting a sufficient number of marked encrypted images from the remaining data hiders.The experimental results show that the embedding rate of the proposed algorithm is improved by 568.56% at the highest and 33.39% at the lowest compared with the related algorithm.After encryption,Share image correlation is as low as 0.0012;The average values of RCE,PSNR,SSIM and NC with dense images are as high as 0.5245,30.08,0.9491 and 0.9996.Compared with related algorithms,this algorithm has obvious advantages in comprehensive performance.