共查询到20条相似文献,搜索用时 15 毫秒
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目的为了提高水印算法的抗几何攻击能力,并兼顾较高的鲁棒性与不可感知性,设计一种基于方向金字塔分解与稳定几何失真校正的鲁棒图像水印算法。方法首先,引入方向金子塔,对载体图像完成分解,输出对应的低通与高通子带;将低通子带分割为一系列的非重叠块;根据载体的亮度、纹理与边缘掩码,计算水印嵌入强度,最大程度地平衡水印图像的不可感知性与鲁棒性;设计水印嵌入方法,将经过Arnold映射加密后的水印嵌入到非重叠子块中,通过修改载体的方向金子塔分解系数,获取水印图像;将不同的攻击类型作用于水印图像,建立训练样本;再利用方向金子塔分解训练样本,计算高通子带的高斯-厄米矩能量,将其视为特征矢量;再利用特征矢量对模糊支持向量机完成训练,以预测几何失真参数,准确校正受攻击的水印图像;设计水印检测机制,从水印图像中复原水印。结果实验数据表明,与当前图像水印方案相比,所提算法具有更高的抗几何变换能力,以及较好的不可感知性与鲁棒性,其提取的水印失真度最小,对应峰值信噪比保持在40dB以上。结论所提水印算法具有较高的鲁棒性和视觉隐秘性,在版权保护、信息防伪等领域具有一定的参考价值。 相似文献
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This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate that, for WOW and S-UNIWARD steganography, the improved features clearly decreased the average test errors of the existing features, and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms. Specifically, when the payload is smaller than 0.2 bpc, the average test error decreases achieve 4% and 3%. 相似文献
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Xiangchun Liu Jing Yu Wei Song Xinping Zhang Lizhi Zhao Antai Wang 《计算机、材料和连续体(英文)》2020,65(2):1385-1395
With the development of satellite technology, the satellite imagery of the
earth’s surface and the whole surface makes it possible to survey surface resources and
master the dynamic changes of the earth with high efficiency and low consumption. As
an important tool for satellite remote sensing image processing, remote sensing image
classification has become a hot topic. According to the natural texture characteristics of
remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm.
The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6.
Our results show that the proposed method is a simpler and more efficient remote sensing
image classification algorithm. It also achieves 99.434% recognition accuracy on SAT-4,
which is 1.5% higher than the 97.95% accuracy achieved by DeepSat. At the same time,
the recognition accuracy of SAT-6 reaches 99.5728%, which is 5.6% higher than
DeepSat’s 93.9%. 相似文献
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Rukiye Karakis 《计算机、材料和连续体(英文)》2023,74(3):4649-4666
Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt. 相似文献
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Local binary pattern (LBP) is one of the most advanced image classification
recognition operators and is commonly used in texture detection area. Research indicates
that LBP also has a good application prospect in steganalysis. However, the existing
LBP-based steganalysis algorithms are only capable to detect the least significant bit
(LSB) and the least significant bit matching (LSBM) algorithms. To solve this problem,
this paper proposes a steganalysis model called msdeLTP, which is based on multi-scale
local ternary patterns (LTP) and derivative filters. The main characteristics of the
msdeLTP are as follows: First, to reduce the interference of image content on features,
the msdeLTP uses derivative filters to acquire residual images on which subsequent
operations are based. Second, instead of LBP features, LTP features are extracted
considering that the LTP feature can exhibit multiple variations in the relationship of
adjacent pixels. Third, LTP features with multiple scales and modes are combined to
show the relationship of neighbor pixels within different radius and along different
directions. Analysis and simulation show that the msdeLTP uses only 2592-dimensional
features and has similar detection accuracy as the spatial rich model (SRM) at the same
time, showing the high steganalysis efficiency of the method. 相似文献
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目的 针对印刷品表面缺陷检测中计算实时性差、缺陷类型识别率不高等问题,提出一种改进灰度共生矩阵(GLCM)的印刷品表面缺陷检测方法。方法 首先对主流的缺陷检测流程进行优化设计,通过对图像进行预处理和差分操作,判断待测印刷品表面是否存在形状缺陷;然后针对传统灰度共生矩阵的特征提取维度高、信息易丢失、旋转不变性差等问题,设计一种综合考虑效率和实时性的缺陷区域特征参数提取算法;最后结合得到的特征参量,通过基于支持向量机的分类器完成不同形状缺陷的分类识别。结果 实验结果表明,文中所设计的改进算法所提取的特征参量更能精确表征缺陷区域的特征,同时,特征参数的提取时间和缺陷分类识别率等指标均比传统检测方法更有优势。结论 在保证计算实时性的前提下,文中所设计的检测方法能有效完成印刷品表面缺陷区域的纹理特征识别能力,具有较高的分类识别率。 相似文献
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为设计出符合消费者感性需求的产品,提出基于支持向量机的产品感性意象值预测方法。先确定产品的感性意象、造型设计要素以及感性评价矩阵。在此基础上,以造型设计要素为自变量,以感性意象评价值为因变量,利用LIBSVM软件,通过对惩罚函数、不敏感损失函数以及核函数等相关参数的分析设置,建立产品感性意象值的预测模型。结合办公座椅进行研究,结果表明支持向量机具有较高的预测精度,所提出的方法是正确可行的。 相似文献
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In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information steganographic scheme based on generative model, compared with the traditional image steganography. The transmitted image is not embedded with any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our scheme has high capacity, security and reliability. 相似文献
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Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa 《计算机、材料和连续体(英文)》2020,62(1):199-216
Aiming at the defects of the traditional fire detection methods, which are
caused by false positives and false negatives in large space buildings, a fire identification
detection method based on video images is proposed. The algorithm first uses the hybrid
Gaussian background modeling method and the RGB color model to perform fire
prejudgment on the video image, which can eliminate most non-fire interferences.
Secondly, the traditional regional growth algorithm is improved and the fire image
segmentation effect is effectively improved. Then, based on the segmented image, the
dynamic and static features of the fire flame are further analyzed and extracted in the area
of the suspected fire flame. Finally, the dynamic features of the extracted fire flame
images were fused and classified by improved fruit fly optimization support vector
machine, and the recognition results were obtained. The video-based fire detection
method proposed in this paper greatly improves the accuracy of fire detection and is
suitable for fire detection and identification in large space scenarios. 相似文献
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针对早期火灾信息特点,提出了一种基于二叉树的最小二乘小波支持向量机(Least squareswavelet support vector machine,LS-WSVM)多类分类方法.该方法首先把主成份分析用于早期火灾信息的特征提取.然后,把二叉树结构和LS-WSVM相结合,提出了基于二叉树的LS-WSVM多类分类模型,不仅避免了盲目分类和不可分情况,而且提高了分类速度和泛化能力.最后,用该模型对特征信息进行处理,从而实现了对早期火灾的多类识别.早期火灾分类实验结果表明,该方法比采用径向基核函数的最小二乘支持向量机多类分类方法具有更好的识别效果和更快的分类速度. 相似文献
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基于四元数矩阵奇异值分解的彩色图像分解 总被引:1,自引:0,他引:1
讨论了四元数矩阵的奇异值分解(QSVD),四元数矩阵的奇异值仍是正实数,但两个酉矩阵是含有四元数的四元数矩阵。给出通过四元数矩阵的等价实矩阵求解QSVD的有效算法。最后应用QSVD进行彩色图像分解,并给出了在Fruits、Baboon等图像上的实验结果。QSVD使许多基于SVD的图像处理方法可以推广到彩色图像处理上而不用再将彩色图像分解成三个通道图像进行处理。 相似文献
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钎焊环检测系统中,采集的图像序列可能具有不完整环或重叠环。通过调整传送带速度和设置固定相机的曝光时间,确定了2幅图像的重叠区域;将图像去噪、灰度化和二值化,得到了具有清晰轮廓的图像;然后结合LSSVM和CA对图像边缘进行了检测,利用互信息法实现了图像配准;最后采用渐入渐出法进行了图像融合,得到了最终的拼接图像。实验表明,这种方法可以快速准确地实现钎焊环图像的拼接。 相似文献
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针对传统方法在低维纳米材料形貌检测和分类鉴别方面的不足,提出了一种基于扫描电子显微镜(SEM)图像的低维纳米材料自动分类方法.以纳米材料的SEM图像为基础,利用小波包分解技术对材料表面纹理特征进行提取,通过将纹理特征与支持向量机(SVM)相结合,实现了纳米材料的自动分类.该方法具有检测速度快、精度高、无损耗等诸多优点,可用于纳米材料大规模生产中的自动检测.对16种不同类别材料的SEM图像仿真结果表明,该方法的分类精度能够达到93.75%,证明了其在实际工程中的有效性. 相似文献
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In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate). 相似文献