A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.

Coverless Image Steganography System Based on Maze Game Generation

The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data concealing has been suggested by researchers called coverless steganography.Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image.This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems.The system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the maze.Firstly,a full grid is generated with several specific rows and columns determined from the number of bits of the secret message.Then,these bits are fed to the full grid to form the maze game stego image.Finally,the generated maze game stego image is sent to the recipient.The experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.

Coverless Steganography for Digital Images Based on a Generative Model

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.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

Coverless Image Steganography Based on Jigsaw Puzzle Image Generation

Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels,creating the stego image.However,the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload.A coverless data hiding concept is proposed to solve this issue.Coverless does not mean that cover is not required,or the payload can be transmitted without a cover.Instead,the payload is embedded by cover generation or a secret message mapping between the cover file and the payload.In this paper,a new coverless image steganography method has been proposed based on the jigsaw puzzle image generation driven by a secret message.Firstly,the image is divided into equal rows then further divided into equal columns,creating blocks(i.e.,sub-images).Then,according to secret message bits and a proposed mapping function,each block will have tabs/blanks to get the shape of a puzzle piece creating a fully shaped jigsaw puzzle stego-image.After that,the generated jigsaw puzzle image is sent to the receiver.Experimental results and analysis show a good performance in the hiding capacity,security,and robustness compared with existing coverless image steganography methods.

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.

A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.

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.

Coverless Text Hiding Method Based on Improved Evaluation Index and One-Bit Embedding

In the field of information hiding,text is less redundant,which leads to less space to hide information and challenging work for researchers.Based on the Markov chain model,this paper proposes an improved evaluation index and onebit embedding coverless text steganography method.In the steganography process,this method did not simply take the transition probability as the optimization basis of the steganography model,but combined it with the sentence length in the corresponding nodes in the model to gauge sentence quality.Based on this,only two optimal conjunctions of the current words are retained in the method to generate sentences of higher quality.Because the size of the training text dataset is generally large,this leads to higher complexity of the steganographic model;hence,fewer repetitions of the generated steganographic sentences occur.Different datasets and methods were selected to test the quality of the model.The results indicate that our method can achieve higher hiding capacity and has better concealment capability.

Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.

面向编码伪装的鲁棒无载体图像隐写方法

传统的图像隐写方法容易受到隐写分析的攻击,无载体图像隐写方法可以从本质上抵抗隐写分析的攻击。然而,现有的无载体图像隐写算法存在鲁棒性低、提取精度有限和不可感知性差等问题,为此提出一种面向编码伪装的鲁棒无载体图像隐写方法。该方案将基于深度的合成隐写方法与传统的聚类相结合,通过感知哈希对编码网络生成的合成图像与相似图像进行匹配,将传输的图像由合成图像替换成真实自然图像以此提高安全性;进一步,利用聚类算法找到与相似图像对应的伪装图像进行传输。以卷积神经网络特征为基准进行聚类,提高了抗几何攻击的能力。实验分析表明,所提方案在隐藏容量、提取精度方面都有较优的表现,且解决了生成式隐写方案存在的图像质量低和鲁棒性差等问题。

基于图像的无载体信息隐藏技术研究

近年来,无载体信息隐藏成为了热门的研究方向,其中,基于图像的无载体信息隐藏算法具有较高的研究价值。文章对图像的无载体信息隐藏的相关算法进行了分类并简要梳理。首先,介绍了算法的图像编码算法,并根据算法评估指标对同一类别的算法进行了分析对比,然后得到该类别算法的优点和不足,最后,总结了图像的无载体信息隐藏存在的问题,给出了未来的研究和改进方向。

结合ACGAN和多重多维混沌系统的无载体信息隐藏

传统的信息隐藏方法总是不可避免修改载体图像,因而难以抵抗各种攻击检测,降低了信息传输的隐蔽性和安全性。为解决此问题,提出了一种基于ACGAN和多重多维混沌系统的无载体信息隐藏方法。首先,基于新四维超混沌系统对原始图像进行复合置乱和DNA加密得到秘密图像并将其作为标签驱动对ACGAN进行训练;然后,利用训练后的ACGAN生成组合图像集对含密标签图像进行排序得到组合图像,并对该组合图像进行加密置乱操作得到最终的加密图像;最后,将训练后的ACGAN与最终加密图像一同进行发送,接收方通过该ACGAN得到相应的图像信息。与现有的信息隐藏方法相比,该方法创新性结合多维混沌系统、DNA加密、ACGAN与无载体信息隐藏,避免了修改原始图像,同时能够有效抵抗各种隐写分析,达到了秘密信息安全传输的目的。

一种基于多重空间的OOXML复合文档无载体信息隐藏方法

现有搜索式无载体信息隐藏方法无需修改载体即可实现秘密信息嵌入,但普遍存在隐藏容量有限的典型问题。基于此,研究分析了OOXML复合文档自身潜在的多重隐藏空间特性,建立了基于多重空间映射的OOXML复合文档无载体信息隐藏模型,提出了基于多重空间的OOXML复合文档无载体信息隐藏方法。从文档组织结构特征和文本内容特征两个角度出发,建立秘密信息与OOXML复合文档源文件位置排列次序与文本音符的双重映射,实现基于OOXML复合文档的信息隐藏。理论分析与实验数据表明,在同等规模文档集的条件下,该方法有效改善了隐藏容量性能。

一种图像块旋转的马赛克拼图伪装隐写

为了提高无载体隐写效率以及隐写鲁棒性,提出了一种图像块旋转的马赛克拼图伪装隐写方法。首先在发送端将共享图像库中利用密钥随机选择的图像缩小并调整色调,再依据秘密信息和密钥对每个图像块添加随机旋转角度形成编码图像,最后将编码图像按规则排列产生含密马赛克图像;接收端依据密钥去除随机旋转,读取编码图像旋转角度获得秘密信息。实验结果表明,所提方法在嵌入容量方面优于其他无载体隐写方法,在鲁棒性方面也有良好的性能。

基于图像Bag-of-Words模型的无载体信息隐藏

介绍一种基于bag-of-words(BOW)模型的无载体信息隐藏方法.该方法使用BOW模型提取图像的视觉关键词(visual words,VW)以表达待隐藏的文本信息,从而实现文本信息在图像中的隐藏.首先使用BOW模型提取图像集中每幅图像的VW,构建文本信息的关键词和VW的映射关系库;然后把每幅图像分为若干子图像,统计每一幅子图像的VW频数直方图,选择频数最高的VW表示该子图像;最后根据构建的文本关键词和子图像VW的映射关系库,搜索出与待隐藏文本信息存在映射关系的子图像序列,将含有这些子图像的图像作为含密图像进行传递.实验结果和分析表明,该隐藏算法在抗隐写分析、鲁棒性和安全性方面均有良好的表现.

基于深度学习的图像隐写研究进展

图像隐写是信息安全领域的研究热点之一.早期隐写方法通过修改载体图像获得含密图像,导致图像统计特性发生变化,因此难以抵抗基于高维统计特征分析的检测.随着深度学习的发展,研究者们提出了许多基于深度学习的图像隐写方法,使像素修改更隐蔽、隐写过程更智能.为了更好地研究图像隐写技术,对基于深度学习的图像隐写方法进行综述.首先根据图像隐写过程,从3个方面分析了基于深度学习的图像隐写方法:1)从生成对抗网络和对抗样本2个角度介绍载体图像获取方法;2)分析基于深度学习的隐写失真设计方法;3)阐述基于编码解码网络的含密图像生成方法.然后,分析和总结了无载体图像隐写方法的优缺点,该类方法无需载体图像即可实现图像隐写,因此在对抗统计分析方面存在天然优势.最后,在深入分析与总结基于深度学习的图像隐写与无载体图像隐写2类方法优缺点的基础上,对图像隐写的发展方向进行了探讨与展望.

一种安全的无载体纹理合成信息隐藏方案

为了有效抵御Zhou等[8]针对Wu等[7]提出的无载体纹理合成信息隐藏方案的攻击,设计出一种基于有限整数网格上拟仿射变换(QATLIG)的纹理合成无载体信息隐藏方案。该方案通过在纹理合成过程中嵌入干扰纹理块,来抵御文献[8]提出的攻击。其中,干扰纹理块的具体位置,由样本纹理源补丁块的坐标通过QATLIG变换而得。同时该方案将QATLIG变换系数纳入密钥中。分析及实验表明,该方案保持了文献[7]方案的生成纹理图像质量,信息隐藏容量损失极小(约1.46%~7.11%),隐藏的信息被暴力破解的概率极低(远小于10-5),可有效抵御文献[8]的攻击方案。

一种可信任的大容量无载体信息隐藏方案

提出一种以线性码原理和混沌映射理论为基础的无载体信息隐藏方案。通过提取图像的像素矩阵,利用线性码原理和混沌映射理论先后对矩阵信息作可验证处理和加密处理,最后通过一系列变换建立图像与密文信息的对应关系,从而实现无载体信息隐藏。实验和分析表明,该方案保证了信息的安全性,增强了随机性,同时具有可验证性,这使得提出的信息隐藏方案是可信任的。另外相比于其他方案,该方案的隐藏容量有了很大的提升,并且大大减少了信息库的存储资源消耗。

基于图像分类的无载体信息隐藏方法

为提高无载体信息隐藏的数据嵌入容量和通信效率,注意到半构造式无载体信息隐藏算法所具有的优势,在仔细分析几种社交平台的用户行为习惯后,提出了一种以社交平台的行为习惯为构造原则的半构造式无载体信息隐藏算法.该算法的具体思想通过构建小图标库中的图标与秘密消息的一一映射关系,将小图标按照一定的原则拼接,完成秘密消息的图像表达,通过传递拼接好的图片,实现秘密消息的传递.为了提高小图标的识别率和整个隐蔽通信系统的抗干扰能力,算法还引入了卷积神经网络对图标库中的图标进行训练和分类,同时在训练时特意引入经过多种攻击方式处理过的小图标作为干扰样本.实验和仿真结果表明,该隐藏方法具备良好的抗攻击能力,隐藏容量和通信效率得到了实质性的提高,可用于实际的隐蔽通信系统.