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基于隐写编码和Markov模型的自适应图像隐写算法 总被引:3,自引:0,他引:3
如何构造大容量、低失真和高统计安全的隐写算法一直是隐写研究的难点和热点.提出一种兼顾感知失真和二阶统计安全的自适应图像隐写算法设计思路.算法将载体各部分的平滑度引入隐写编码的生成过程,自适应地利用一簇隐写编码在载体各部分的合理运用降低载密图像失真度;在隐秘信息嵌入方式上利用基于Markov链模型的动态补偿方法提高载密图像统计安全性;算法对载体最低有效位和次最低有效位进行嵌入以保证嵌入容量.实验表明算法在相同嵌入量下相较双层随机LSB匹配算法以及仅使用一种隐写编码的算法,失真度更低且载体统计分布的改变更小,而在失真度和统计分布改变相近时嵌入容量更大. 相似文献
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如何设计高阶统计安全的大容量隐写算法是当前隐写研究的难点和热点。该文基于Markov链安全性指标和动态补偿的思路,提出一种二阶统计保持的隐写算法。该算法在不降低嵌入量的前提下尽量保持了载体图像的二阶统计特性。实验结果表明,该算法在较大容量数据嵌入过程中,能较好保持二阶统计特性,取得隐写安全性的提高。 相似文献
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针对LSB匹配隐写的图像复原隐写分析 总被引:3,自引:0,他引:3
最低有效位(LSB)匹配隐写是目前图像隐写分析的难点和热点之一.为了提高针对LSB匹配隐写的隐写分析算法性能,将图像退化复原理论与图像隐写分析相结合,提出一种新的隐写分析算法.首先将LSB匹配隐写过程建模为加性噪声造成的图像退化过程,提出了一种专用复原滤波算法;然后将载密图像的复原图像作为载体图像的估计图像,提取载密图像与估计图像的质心特征,结合Fisher线性判决器实现隐写分析.实验结果表明,复原滤波算法可有效地复原受LSB匹配隐写噪声污染的退化图像,隐写分析算法的总体性能优于Ker方法,尤其在低嵌入率条件下表现良好,适用于空间域图像LSB匹配隐写. 相似文献
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在图像边缘自适应LSB匹配改进隐写算法中,秘密信息嵌入位置的选择仅由某个方向上像素对的差值决定,未考虑该像素与其邻域内其他像素的差值变化的特点.针对该问题,对隐写前后图像的八方向差分直方图进行分析,提出一种基于LSB匹配改进算法(LSBMR)边缘自适应隐写检测的算法.该算法计算图像的八方向绝对差分直方图,提取直方图中隐写前后变化较为明显的频数用以构建特征向量,并使用支持向量机完成检测.对较低嵌入率下(≤0.5 bpp)的EALSBMR隐写结果进行检测,结果表明该算法的平均检测率均高于现有典型的隐写分析算法. 相似文献
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针对隐写算法安全性的问题,提出一种结合代数多重网格(AMG)的钻石编码(DE)隐写算法。首先,通过AMG方法将图像的像素点分成粗细网格两个部分。然后,结合DE把机密信息分别嵌入到粗细网格两个像素序列中。其中,粗网格部分像素的改变对整幅图像的质量影响较小,而细网格部分像素的改变对整幅图像的质量影响较大。又因为DE的k值跟信息隐藏容量密切相关,随着k值的增加像素改变量变大,所以用DE嵌入的过程中,粗网格部分选择的k值不小于细网格。最后,选择DE的k值等于1与2,提出了三种隐写方案。与最低有效位(LSB)置换、随机LSB匹配、DE算法和自适应边缘检测算法进行比较,实验结果表明,三种隐写方案的一阶Markov安全指标皆优于其他对比隐写算法。 相似文献
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当前隐写分析算法多利用载体高阶统计特性进行分析,因此如何准确反映隐写对载体高阶统计特性的影响,即对隐写算法统计安全性进行合理评估是隐写研究的重点之一。文章提出数字图像隐写高阶Markov链统计分布模型,在对常用图像扫描方法所构成的高阶Markov链包含相邻像素相关性信息的程度进行比较后,采用Hilbert扫描方式构建数字图像n阶Markov链模型,进而研究隐写对该模型经验矩阵的影响,提出数字图像隐写统计安全性的n阶Markov链测度,并证明其有界且在特定情况下与ε-secure安全性指标等价。实验说明了文章所提模型经验矩阵在隐写前后的改变情况,统计分布测度比较实验证实了该模型统计分布测度较ε-secure安全性指标和图像Markov链模型统计分布测度对隐写引起载体高阶统计分布改变的反映更为充分。 相似文献
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Steganalysis always concentrates on the detection of the existence of the hidden information embedded into some digital multimedia by steganography. This paper presents a novel steganographic payload location method for spatial least significant bit (LSB) steganography with the JPEG-decompressed images used as the cover images. Firstly, we discuss the possibility of recovering the original cover image and compute the probability of successfully recovering. Then, we compute the distributions of the difference values between the estimated cover pixels and stego pixels in the payload-carrying and no-payload-carrying positions. Finally, combining with the theory of hypothesis test, we present a method to locate the payload-carrying pixels of JPEG-decompressed images, which is suitable for both LSB replacement and LSB matching. The experimental results show that: 1) the number of stego images needed to accurately locate all the payload-carrying pixels in the proposed method approaches the lower bound; 2) given the number of stego images, in the proposed method, the number of the payload-carrying pixels precisely located approaches the upper bound; 3) the proposed method performs better than some current state-of-the-art steganographic payload location methods for JPEG-decompressed images. 相似文献
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At present, steganalysis research focuses on detecting the existence of a hidden message. However, extracting the hidden information, i.e., an extracting attack, is crucial in obtaining effective evidence in computer forensics. Due to the difficulty of an extracting attack, research in this field is limited. In steganography with a stego key, an extracting attack is equivalent to recovering the stego key. In this paper we study a method for recovering the stego key in least significant bit (LSB) steganography with a decompressed JPEG image as the cover image. Firstly, the recovery of the stego key is translated into a cryptanalysis problem for a sequential cipher. The method for recovering the stego key is based on estimating the modification positions. The minimum size of the data used to recover the stego key successfully is discussed. Secondly, when a decompressed JPEG image is used as the cover image, the probability of recovering the cover pixels using recompression is discussed. Recompression is used to compute the error of the estimated sequence. Finally, an algorithm to recover the stego key in LSB steganography with a decompressed JPEG image as the cover image is proposed. The experimental results for the steganographic software, Hide and Seek 4.1 and its variant, which is a typical representative of LSB steganography, show that the proposed method can successfully recover the stego key in LSB replacement and LSB matching, i.e., the extracting attack is successful, and it outperforms three previous methods in terms of computational complexity. 相似文献
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在分析图像盲检测算法原理的基础上,提出了一种在形态小波高频系数上进行消息嵌入的抗盲检测隐写算法.该算法利用五株采样提升实现图像的小波变换,在大于一定门限的小波高频系数中嵌入消息,并通过建立的嵌入信息表来修正嵌入规则以保持小波系数直方图近似不变,在门限处引入直方图调整策略以减小系数直方图在门限处的变化.由于通用盲检测算法大多基于概率密度函数的变化实现图像隐写的检测,因此本文算法可以获得对通用盲检测算法的抵抗能力.实验结果表明,本文算法在抵抗小波高阶统计量分析、直方图特征函数质心等盲检测算法能力方面,优于LSB匹配、像素值差分等隐写算法. 相似文献
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In this paper, a novel steganographic method is proposed employing an immune programming strategy to find a near-optimal solution for the pair-wise least-significant-bit (LSB) matching scheme. The LSB matching method proposed by Mielikaien utilizes a binary function to reduce the number of changed pixel values. However, his method still has room for improvement. A tier-score system is proposed in this paper to assess the performance of different orders for LSB matching. An immune programming approach is adopted to search for a near-optimal solution among all the permutation orders. The proposed method can reduce the distortion of the stego image, improve the visual quality, and decrease the probability of detection. The experimental results show that the proposed method achieves better performance than Mielikainen’s pair-wise LSB matching method in terms of distortion and survival probability against steganalysis. 相似文献
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Zihan Wang Neng Gao Xin Wang Ji Xiang Daren Zha Linghui Li 《Computer Graphics Forum》2019,38(7):393-401
Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels. 相似文献
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目的 针对目前信息隐藏的嵌入和提取函数为固定表达式,存在容易被隐写分析和非法提取信息的安全隐患,以及基于模函数的隐写研究现状,提出信息隐藏参数化设计思想、优化参数化二元模映射隐写算法。方法 首先提出信息隐藏参数化设计定义和分析参数化信息隐藏算法的安全性,然后提出优化参数化二元模映射隐写算法。优化参数化二元模映射隐写算法将两个像素值优化组合后的模运算结果映射到一位n2进制信息,从而实现信息隐藏。结果 优化参数化二元模映射隐写算法的密钥空间大,载密图像均方差小于或等于同类算法。结论 信息隐藏参数化设计可以有效提高信息隐藏算法的抗隐写分析能力和抗信息提取能力;优化参数化二元模映射隐写算法与同类算法相比,具有更好的载密图像视觉质量和安全性。 相似文献