图像隐藏技术在当今大数据时代扮演着重要角色, 尤其在版权保护和保密通信等领域中有着巨大的应用价值。提出了一种基于计算鬼成像的图像隐藏方法, 目标图像经过计算鬼成像系统加密, 随机散斑作为系统密钥, 将密文进行二值化后的结果采用最低有效位算法嵌入到宿主图像中, 利用相应的水印提取规则和二阶相关算法恢复目标图像。仿真结果表明, 该方法具有较好的隐蔽性, 嵌入水印图像的峰值信噪比可以达到50 dB, 授权用户可以用密钥获得目标图像, 同时, 所提方案有一定的容错能力, 对椒盐噪声和图像裁剪具有一定的鲁棒性。

针对可逆数据隐藏中图像质量和嵌入容量之间相互矛盾的问题, 提出一种中心折叠技术与最低有效位替换相结合的可逆数据隐藏方法。该方法首先对原始图像中的像素进行位平面划分, 以得到2个位平面; 然后利用中心折叠技术将秘密数据折叠, 并将折叠后的秘密数据嵌入到原始像素的最高有效位中, 以生成2个隐秘像素; 最后利用最低有效位替换将秘密数据嵌入到隐秘像素的最低有效位中, 以进一步提高嵌入容量。实验结果表明, 利用中心折叠技术与最低有效位替换来嵌入秘密数据, 嵌入容量达到了1 572 778位, 有效地提高了嵌入容量; 当嵌入容量相同时, 与嵌入秘密数据后的相关算法相比, 图像的PSNR提高了约2 dB, 降低了图像失真, 具有更好的不可感知性。

Recently, automatic diagnosis of diabetic retinopathy (DR) from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME) is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS) based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF) is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB) for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP) and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA) to reduce time complexity. Based on some performance metrics, the PSODEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM) is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

针对信息安全领域隐写技术存在的感知失真过大和图像源压缩率高造成误差率高的问题, 设计了一种基于Karhunen-Loeve transform (KLT)优化的图像隐写术。通过迭代聚类算法求解协方差矩阵与聚类平均值,对相关数值 进行调整分割图像,引入KLT算法压缩图像数据,用最低有效位代替加密数据来隐藏数据,提 取信息时采用反向线性变换运算及原始像素矩阵获得有效的隐藏图像信息。结果表明,与常用 算法相比该方法提取的图像有较高容量、较大信噪比及较小数据失真。

利用最低有效位的思想,提出了一个量子图像水印方案。在该方案中,嵌入 者通过置换载体图像灰度 值的某一个比特把水印图像嵌入到量子载体图像的某些像素中,这些像素由一个私钥决定。只有拥有这个私 钥的版权者才可以在不知道初始载体图像的前提下提取出水印信息。分别设计了嵌入和提取算法的量子电路 图,并给出了该水印算法的计算机仿真。通过比较嵌入载体图像和初始载体图像之间的峰值信噪比,说明 提出的水印方案实现了不可见性。实验结果也表明,提出的水印方案具有良好的可伸缩性。