基于FCM和HVS的密写方法

为了提供较大的秘密信息嵌入量和保持良好的载密图像质量,该文提出一种基于模糊C均值聚类和人眼视觉特性的图像密写。该密写方法将对比度和梯度敏感度作为特征向量,并通过模糊C均值聚类将像素分为视觉敏感类与视觉不敏感类,将较多秘密信息嵌入属于视觉不敏感类的像素,而将较少秘密信息嵌入属于视觉敏感类的像素。实验结果表明,与四边匹配算法相比,该算法有更大的嵌入量并保持了良好的载密图像质量。     

SOM和小波对比度在密写中的应用

为了提供较大的秘密信息嵌入量和保持较好的载密图像质量,提出了一种基于自组织特征映射神经网络和小波对比度的图像密写。该方法先将载体图像分成固定大小的小块,对每一小块进行小波一级分解后计算小波对比度。然后,利用自组织特征映射神经网络将小块分为三类。最后,采用模算子技术嵌入秘密信息。实验结果表明,与WCL算法相比,该算法有更大的嵌入量并保持了良好的载密图像质量。     

基于小波对比度和神经网络的图像隐写方法

为使通信安全在传输过程中提供较大的秘密信息嵌入量,并保持较好的载密图像质量,提出一种基于自组织特征映射神经网络和小波对比度的图像隐写方法。将载体图像分成固定大小的小块,采用小波一级分解并计算其小波对比度,利用自组织特征映射神经网络将小块分为3类,采用模算子技术嵌入秘密信息。实验结果表明,该方法有较大的嵌入量并保持良好的载密图像质量。     

一种基于FCM和HVS的隐写方法

为了提供较大的秘密信息嵌入量并保持良好的载密图像质量,提出了一种基于FCM（模糊聚类）和HVS（人眼视觉特性）的图像隐写方法。该方法将对比度和梯度敏感度作为特征向量,并通过模糊C均值聚类将像素分为视觉敏感类与视觉不敏感类;采用模算子隐写法将较多秘密信息嵌入属于视觉不敏感类的像素,而将较少秘密信息嵌入属于视觉敏感类的像素。实验结果表明,与SMFH方法相比,该方法具有更大的嵌入量。     

基于小波对比度和神经网络的图像隐写方法

为使通信安全在传输过程中提供较大的秘密信息嵌入量,并保持较好的载密图像质量,提出一种基于自组织特征映射神经网络和小波对比度的图像隐写方法。将载体图像分成固定大小的小块,采用小波一级分解并计算其小波对比度,利用自组织特征映射神经网络将小块分为3类,采用模算子技术嵌入秘密信息。实验结果表明,该方法有较大的嵌入量并保持良好的载密图像质量。     

基于HVS和SOM的小波域隐写

为了提供较大的秘密信息嵌入量并保持良好的载密图像质量,提出一种基于SOM(自组织特征映射神经网络)和HVS(人眼视觉特性)的小波域图像隐写.该方法先将载体图像分成固定大小的小块,以小波对比度之和以及小波系数方差作为特征量.然后,利用SOM将小块分为三类.最后,采用模算子隐写将秘密信息嵌入到小波系数中.实验结果表明,与WHS算法相比,该算法有更大的嵌入量并保持了良好的载密图像质量.     

利用模运算及其周期性特点的安全隐写算法

为了提供较大的可调信息嵌入量和保持载密图像良好的视觉质量,提出一种基于模运算及其周期性特点的安全隐写算法。首先将秘密信息流转为n进制信息流表示,然后根据余数循环的特点,应用具有安全特性的模函数将一位n进制信息嵌入载体图像的一个像素中,直至信息嵌入完毕。选择不同的参数n可以得到不同的嵌入率和载密图像视觉质量。理论分析和实验结果表明,本文算法在保持高嵌入率的同时仍保持较好的载密图像视觉质量,且安全性好;其信息嵌入率选择具有高度弹性,可以根据实际要求选择在嵌入率和载密图像质量都很理想的隐写方案。与同类算法相比具有更强的实用性。     

基于自组织竞争神经网络和小波变换的密写

依照人眼视觉特性对边缘和较暗区域不敏感的特点,提出基于自组织竞争神经网络和小波变换的图像密写方法.该方法将载体图像分成若干固定大小的子块,对各子块进行一级小波分解后计算小波对比度和大幅值小波系数的数量,利用自组织竞争神经网络将各子块分为三类,并采用模算子技术嵌入秘密信息.实验结果表明,该方法在较好地保持原载密图像视觉效果的同时可以嵌入更多的秘密信息.     

基于小波对比度和模函数的安全密写算法

针对图像隐写中大容量、高保真度和安全性问题,依据人眼对变换剧烈及较暗区域均不敏感的视觉特点,结合模函数周期性运算,提出一种基于小波对比度和模函数运算的高保真图像隐写算法。该方法首先将载体图像按固定大小分块并计算其小波对比度,然后依据小波对比度确定各块嵌入深度,最后采用模函数运算嵌入秘密信息。实验结果表明,该方法能嵌入较多信息和保持良好的载密图像质量。     

基于视觉感知与菱形编码的图像隐写算法

为提供较大的隐写容量和保持良好的载密图像质量,提出一种基于视觉感知和菱形编码的图像隐写算法。根据人类视觉系统,针对人眼对图像的高中低灰度区、纹理区及平滑区的敏感度的不同,在不同区域选择嵌入不同数量的秘密信息;同时为了尽可能减少对图像像素值的改变,采用改进的菱形编码来嵌入秘密信息。实验结果表明,与HVS-OPAP算法相比,该算法图像的嵌入容量提高了4.09%,获取的隐写图像具有更好的不可感知性,同时峰值信噪比平均提高了1.5dB,而且该算法具有抗RS隐写分析能力,安全性更高。     

Inverted pattern approach to improve image quality of information hiding by LSB substitution

Capacity and invisibility are two targets of the methods for information hiding. Because these two targets contradict each other, to hide large messages into the cover image and remain invisible is an interesting challenge. The simple least-significant-bit (LSB) substitution approach, which embeds secret messages into the LSB of pixels in cover images, usually embeds huge secret messages. After a large message is embedded, the quality of the stego-image will be significantly degraded. In this paper, a new LSB-based method, called the inverted pattern (IP) LSB substitution approach, is proposed to improve the quality of the stego-image. Each section of secret images is determined to be inverted or not inverted before it is embedded. The decisions are recorded by an IP for the purpose of extracting data and the pattern can be seen as a secret key or an extra data to be re-embedded. The experimental results show that our proposed method runs fast and has better results than that of previous works.     

A high quality steganographic method with pixel-value differencing and modulus function

In this paper, we shall propose a new image steganographic technique capable of producing a secret-embedded image that is totally indistinguishable from the original image by the human eye. In addition, our new method avoids the falling-off-boundary problem by using pixel-value differencing and the modulus function. First, we derive a difference value from two consecutive pixels by utilizing the pixel-value differencing technique (PVD). The hiding capacity of the two consecutive pixels depends on the difference value. In other words, the smoother area is, the less secret data can be hidden; on the contrary, the more edges an area has, the more secret data can be embedded. This way, the stego-image quality degradation is more imperceptible to the human eye. Second, the remainder of the two consecutive pixels can be computed by using the modulus operation, and then secret data can be embedded into the two pixels by modifying their remainder. In our scheme, there is an optimal approach to alter the remainder so as to greatly reduce the image distortion caused by the hiding of the secret data. The values of the two consecutive pixels are scarcely changed after the embedding of the secret message by the proposed optimal alteration algorithm. Experimental results have also demonstrated that the proposed scheme is secure against the RS detection attack.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.