基于灰度图像的像素量化隐写算法

目的为了提高灰度图像处理的规模、嵌入量、安全性及鲁棒性,提出一种基于灰度图像的像素量化隐写算法。方法首先对载体图像进行Hilbert曲线扫描和图像像素量化,将量化信息产生的阈值区域准确地划分标记,然后分别对载体图像、待嵌入信息执行Hilbert置乱和D-S(data encryption security algorithm)数据加密处理,最后将加密后产生的秘密信息和相关辅助信息嵌入密文图像内。结果实验结果表明,该算法的嵌入率相较于原算法提升了10%,接收者能通过密钥解密获取原始信息,还原完整的载体图像。结论该隐写算法信息嵌入率高,鲁棒性和安全性较强。     

Coverless Image Steganography Based on Image Segmentation

To resist the risk of the stego-image being maliciously altered during transmission, we propose a coverless image steganography method based on image segmentation. Most existing coverless steganography methods are based on whole feature mapping, which has poor robustness when facing geometric attacks, because the contents in the image are easy to lost. To solve this problem, we use ResNet to extract semantic features, and segment the object areas from the image through Mask RCNN for information hiding. These selected object areas have ethical structural integrity and are not located in the visual center of the image, reducing the information loss of malicious attacks. Then, these object areas will be binarized to generate hash sequences for information mapping. In transmission, only a set of stego-images unrelated to the secret information are transmitted, so it can fundamentally resist steganalysis. At the same time, since both Mask RCNN and ResNet have excellent robustness, pre-training the model through supervised learning can achieve good performance. The robust hash algorithm can also resist attacks during transmission. Although image segmentation will reduce the capacity, multiple object areas can be extracted from an image to ensure the capacity to a certain extent. Experimental results show that compared with other coverless image steganography methods, our method is more robust when facing geometric attacks.     

一种基于改进量化表的 JPEG 图像压缩算法

目的结合人眼视觉特性,研究一种基于改进量化表的JPEG图像压缩算法(JPEG-HVS)。方法利用人眼亮度对比度敏感函数(CSF)生成一种新的量化表,来代替传统JPEG标准推荐的亮度量化表,并通过Matlab7.0对不同种类图像进行了仿真实验。通过计算不同种类图像的压缩质量评价指标,将提出的压缩算法与传统JPEG压缩算法及JPEG区域法进行对比。结果 JPEG-HVS实现的压缩比比JPEG实现的压缩比平均高出53.56%,比JPEG区域法平均高出18.75%。3种压缩方法的峰值信噪比(PSNR)波动不大,JPEG的PSNR值最大,JPEG-HVS次之,平均结构相似度(MSSIM)从大到小排列依次为JPEGJPEG-HVSJPEG区域法。JPEG-HVS编解码所需时间要明显少于JPEG。同时依靠主观评价可以发现,经JPEG-HVS解压的重构图像仍具有良好的视觉特性。结论在保证了压缩质量的同时,提出的JPEG-HVS压缩算法相比于传统JPEG压缩算法、JPEG区域法,可以实现更大的压缩比和更快的编解码速度,更有利于图像的存储与传输。     

基于学习矢量量化的运动目标检测算法

提出一种基于学习矢量量化的运动目标检测算法.通过训练样本,网络能自适应地确定区分运动目标和背景的阈值向量.输入向量包含图像的 YCbCr 颜色空间分量和灰度共生矩阵的方向特征.两者融合到算法中,有效抑制了背景亮度变化对运动目标检测的干扰.仿真实验结果表明,即使在背景模型亮度剧烈变化的情况下,算法也能够准确检测出运动目标.     

基于双信息统计与引力聚类的图像篡改检测算法

目的 为了解决当前图像复制-粘贴篡改检测算法的鲁棒性与检测精准度不佳等问题。方法 将图像的颜色信息引入伪造检测过程,提出双信息统计机制耦合引力聚类的图像复制-粘贴篡改检测算法。首先,利用Hessian矩阵来准确提取图像的特征点。然后,利用图像的梯度直方图来描述图像的方向特征,并联合图像的颜色信息,构造双信息统计机制,获取图像的特征向量。计算特征向量间的欧氏距离,构造近似测量模型,对图像特征进行匹配。最后,利用引力聚类方法,实现图像特征点的聚类,精准检测复制-粘贴篡改内容。结果 与当前图像复制-粘贴篡改检测方法相比,所提算法具有更高的检测精准度,以及更好的鲁棒性。结论 所提方案可以准确检测并定位出伪造内容,在图像水印、信息安全领域具有一定的参考价值。     

多尺度形态学图像边缘检测方法

在形态学边缘检测算子的基础上,综合形态膨胀和形态腐蚀,得到修正的边缘检测算子,以减轻图像边缘检测的模糊性;进行形态结构元素尺度调整,并综合各种尺度下的边缘特征,得到噪声存在条件下较为理想的图像边缘。实验验证了该算法的可行性和有效性。     

基于图像尺寸变换的角点检测算法研究

为了研究基于尺寸变换的角点检测技术的检测效果,基于图像缩小法的角点检测技术,提出了图像放大法的角点检测技术。详细介绍了两种检测技术的计算原理并进行了实验验证,结果表明这两种方法都是行之有效的。在此基础上,将这两种方法与不进行图像尺寸变换的角点检测技术进行了对比研究,结果表明,基于尺寸变换的角点检测技术与不进行图像尺寸变化的角点检测技术的检测结果精度基本相同,因此,在角点的实际检测计算中,无需对被检测图像进行尺寸变换处理。     

基于压缩量化与邻域空间LBP算子的图像哈希算法

目的为了解决哈希算法的感知鲁棒性与伪造检测能力不高的问题,提出基于特征压缩机制与邻域空间局部二值模式的紧凑图像哈希算法。方法首先利用2D线性插值技术,对输入图像进行预处理;嵌入Ring分割技术,将其变为二次图像;再利用Gabor滤波技术对其完成过滤;考虑到图像的颜色特征与其内在的空间关系,基于局部二值模式LBP设计邻域空间LBP算子,提取滤波图像的特征;构建特征压缩量化准则,输出紧凑的哈希二值数组;迭代Logistic映射,输出随机序列,通过量化每个序列值输出密钥流,通过构建动态引擎设计分段异加密模型,实现紧凑哈希序列的加密,获取图像哈希;最后计算原始哈希序列与待检测哈希序列的Hamming距离,实现图像信息的安全认证。结果与已有的哈希生成机制相比,文中算法所输出的哈希序列更紧凑,对旋转、伽马校正等篡改操作具有更好的感知鲁棒。结论所提哈希技术具备较高的安全性,在包装图标检索、信息检测等领域具有较好的价值。     

A Novel Steganography Algorithm Based on Instance Segmentation

Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion. We take advantage of the complexity of the object texture and consider that under certain circumstances, the object texture is more complex than the background of the image, so the foreground object is more suitable for steganography than the background. On the basis of instance segmentation, such as Mask R-CNN, the proposed method hides secret information into each object's region by using the masks of instance segmentation, thus realizing the information hiding of the foreground object without background. This method not only makes it more efficient for the receiver to extract information, but also proves to be more secure and robust by experiments.     

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.     

一种改进的Roberts和灰色关联分析的边缘检测算法

针对Roberts算子容易漏检及对噪声较敏感的问题,提出一种改进的基于灰色关联分析方法的边缘检测算法GRA-Roberts.通过改进Roberts算子提高其检测精度和抗噪性能,结合灰色关联分析方法,将改进后的Roberts算子作为参考序列,通过计算灰色关联度来检测图像边缘.实验结果表明,GRA-Roberts算法能有效克服Roberts算子算法易漏检和对噪声敏感的问题,且比其他灰色关联结合算法具有更好的检测效果.     

Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user. The user can extract the privacy information successfully with a similar method of feature analysis and index construction. The simulation results show good performance of robustness. And the hiding success rate also shows good feasibility and practicability for application. Since the medical images are kept original without embedding and modification, the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.     

基于图像纹理特征的目标快速检索

在讨论共生矩阵的基础上,提出了一个通过图像分割获取目标图像纹理特征,进而实现图像快速检索的方法。试验表明,该方法检索目标图像的可靠性较高,具有良好的应用价值。     

一种结合人脸检测的多码率小波图象编码方法

在结合人脸检测的基础上，提出了一种多码率的小波图象编码方法，该方法分利用了人眼的视觉特性，强调人眼对图象的主观感受，高压比时恢复图象仍能保持较好的主观质量。     

基于统计模式识别的彩色图象分割方法

根据岩芯铸体薄片图象特点,提出了用颜色空间作为特征空间,利用统计模式识别的监督分类方法,最小距法则来对彩色图象进行真彩色二值化分割。实际结果证明该方法具有快,有效和准确的特点,对于彩色岩芯图象的分割处理十分有效。     

基于小波变换的图像边缘检测算法的研究

在对经典的图像边缘检测算法深入分析的基础上,进一步完善了基于小波变换的边缘检 测算法.随尺度增加,边缘和噪声的小波幅值表现出不同变化特性,从而在低信噪比的信号中检测出噪声和边缘.实验结果表明,基于小波变换的图像边缘检测算法能够比其它算法保留更多的图边缘信息,鉴别出伪边缘,其精度能够满足基于图像处理技术的各种质量检测系统的要求.     

A Fusion Steganographic Algorithm Based on Faster R-CNN

The aim of information hiding is to embed the secret message in a normal cover media such as image, video, voice or text, and then the secret message is transmitted through the transmission of the cover media. The secret message should not be damaged on the process of the cover media. In order to ensure the invisibility of secret message, complex texture objects should be chosen for embedding information. In this paper, an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message. Firstly, complex texture regions are selected based on a kind of objects detection algorithm. Secondly, three different steganographic methods were used to hide secret message into the selected block region. Experimental results show that the approach enhances the security and robustness.     

基于2k修正和Canny边缘检测的图像隐写

针对信息安全,提出基于Canny边缘检测和2k修正的信息隐藏算法.秘密信息仅仅隐藏在边界像素上,首先利用Canny边缘检测算子检测载体图像的边缘,然后利用伪随机数序列对边缘像素进行重排.为了增强秘密信息的安全性,采用霍夫曼编码把秘密信息变成二进制位流.每个像素隐藏的位数由其灰度值确定.最后,为了增强隐写图像的视觉效果,采用2k修正法进一步减小隐写图像和载体图像之间的误差.实验证明提出的方法比LSB-2和文献[12]的方法更好.     

基于SVM的ECT图像重建算法

电容层析成像（ECT）技术是基于电容敏感机理的过程层析成像技术。ECT的图像重建是一个典型的有限样本非线性映射问题。支持向量机（SVM）作为一种小样本处理方法,具有较强的泛化能力,被认为是目前针对小样本分类问题的最佳理论。提出了一种基于SVM的四层神经网络的图像重建算法,仿真结果表明,该算法用于三相流图像重建具有较强的空间分辨率和泛化能力。     

一种边界-区域相结合的图像分割算法

提出了一种模仿人类视觉机制的区域-细节的图像分割算法.首先提取图像边缘,然后将边缘分段切割,得到端点集合,然后从端点集合生成Delaunay三角形网络,以Delatmay三角形为顶点,相邻三角形的属性差异作为边的权重,构造图;9以基于图的分割算法生成最小生成树,划分区域.最后用Snake模型精确确定区域边界,生成准确的区域边缘.实验证明,这种区域分割和边缘检测相结合的方法能准确地分割非纹理图像,较好地克服了块现象和非连续边界,相比单一区域分割或者边缘检测方法有更好的分割结果,并且计算速度比较快.