HEVC video information hiding scheme based on adaptive double-layer embedding strategy

High Efficiency Video Coding (HEVC) is well-known as an internationally popular video coding standard, and HEVC-based steganography has received increasing attention. In this paper, a new adaptive HEVC video information hiding method based on Prediction Unit (PU) partition mode and double-layer embedding strategy is proposed. Double-layer embedding is a method to complete the first-layer embedding using the mapping rules of PU partition mode, and to perform the second-layer embedding after the first-layer embedding. The cost assignment function designed in this paper can accurately evaluate the second-layer data embedding distortion. The frame position, motion properties and block size of PU are taken into consideration for the second-layer data embedding, and the syndrome-trellis codes (STCs) are used to minimize the embedding distortion. Experimental results show that the proposed adaptive double-layer embedding algorithm has better embedding efficiency and less embedding distortion in most cases.     

基于修改概率转换和非加性嵌入失真的视频隐写方法

近年来,基于运动矢量的视频隐写引起了信息隐藏领域研究者的广泛关注。许多视频隐写方法通过合理地对运动矢量定义加性嵌入失真函数获得了良好的性能,然而这些方法忽略了载体元素之间的相互嵌入影响。该文提出的利用非加性嵌入失真的视频隐写方法为运动矢量设计了可以反映相互嵌入影响的联合嵌入失真,并通过分解联合失真实现修改概率的转换,从而动态、合理地在运动矢量的水平分量和垂直分量分配秘密消息。实验结果表明,与使用加性嵌入失真方法相比,该方法能获得更好的安全性和率失真性能。     

语音隐藏的研究及实现

本文提出了一种基于伪装式信息隐藏安全技术的语音信息隐藏电话（SIHT）系统的研究和实现方法。给出了语音信息隐藏的模型，并进行了模拟仿真。SIHT采用基于DSP的嵌入式技术设计了相应的功能模块。它可以利用普通的模拟电话线路，经过公共电话交换网络（PSTN），在不引起人们注意的普通通话中，通过系统建立的一条隐秘信道将保密信息顺利地传递到接收方。实验证明，SIHT可以在保证语音质量的条件下实现实时语音信息隐藏保密通信的功能。     

Steganalysis of Network Packet Length Based Data Hiding

Recently, data hiding by modifying network parameters like packet header, payload, and packet length has become popular among researchers. Different algorithms have been proposed during the last few years which have altered the network packets in different ways to embed the data bits. Some of these algorithms modify the network packet length for embedding. Although most of the packet length based embedding schemes try to imitate the normal network traffic distribution, they have altered the statistical distribution of network packet lengths during embedding. These statistical anomalies can be exploited to detect such schemes. In this paper, a second order detection scheme for packet length based steganography has been proposed. A comprehensive set of experiments have been carried out to show that the proposed detection scheme can detect network packet length based steganography with a considerably high accuracy.     

CNN-based steganalysis and parametric adversarial embedding:A game-theoretic framework

CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can be restored by retraining the CNN with adversarial stego images. A problem with this model is that, arguably, at training time the steganalyzer is not aware of the exact parameters used by the steganographer for adversarial embedding and, vice versa, the steganographer does not know how the images that will be used to train the steganalyzer are generated. In order to exit this apparent deadlock, we introduce a game theoretic framework wherein the problem of setting the parameters of the steganalyst and the steganographer is solved in a strategic way. More specifically, we propose two slightly different game-theoretic formulations of the above problem, the difference between the two games corresponding to the way the output of the steganalyzer network is thresholded to make the final decision. In both cases, the goal of the steganographer is to increase the missed detection probability, while the steganalyst aims at reducing the overall error probability in the first case, and the missed detection probability for a given false alarm rate, in the second one.We instantiated the two games by considering a specific adversarial embedding scheme (namely a modified version of the adversarial embedding scheme proposed by Tang et al. (2019), and we run several experiments to derive the equilibrium points and the corresponding payoff for the two versions of the game. By comparing the error probabilities at the equilibrium, with those obtained by using other strategies, like the adoption of a worst case assumption or the use of the adversarial embedding scheme by Tang et al. (2019), the benefits of addressing the interplay between the steganographer and the steganalyst in a game-theoretic fashion come out.     

基于比特控制的快速矩阵嵌入

为了保证安全和信息传输率,隐写术期望能利用对载体的每个修改嵌入尽可能多的信息,也即提高嵌入效率。矩阵嵌入是最主要的提高嵌入效率的编码方法。Fridrich提出的基于随机线性码的矩阵嵌入方法能达到高的嵌入效率,但是计算复杂度较高。该文利用择多校验法对矩阵嵌入进行改进,首先使用控制比特对载体分组进行异或构造新载体,然后在新载体上执行矩阵嵌入,通过考察控制比特的状态可以快速生成修改量尽可能小的模式。分析和实验表明,该方法可以在嵌入效率和嵌入速度之间灵活的进行折中。与Fridrich的原始方法相比,新方法在保持嵌入效率基本不变的情况下,其计算复杂度随着控制比特数的增加以指数速度降低。与已有的快速矩阵嵌入方法比,新方法能以更快的嵌入速度达到更高的嵌入效率。     

Adaptive JPEG steganography based on distortion cost updating

Previous adaptive JPEG steganography algorithms mostly calculate image distortion before secret information embedding,so they can’t dynamically adjust distortion costs.Considering the mutual impacts during embedding,an adaptive JPEG steganography algorithm based on distortion cost updating was proposed.Firstly,three factors that affect embedding fluctuations were analyzed,including quantization step,the absolute value of the quantized DCT coefficient to be modified and perturbation error.Then the embedding update strategy (EUS) was proposed,which enabled to dynamically update the distortion costs.After that,an adaptive JPEG steganography algorithm was implemented combining the strategy.The experimental result illustrates that the algorithm can significantly improve the secure performance of JPEG steganography algorithm.     

DGANS:robustness image steganography model based on double GAN

Deep convolutional neural networks can be effectively applied to large-capacity image steganography,but the research on their robustness is rarely reported.The DGANS (double-GAN-based steganography) applies the deep learning framework in image steganography,which is optimized to resist small geometric distortions so as to improve the model’s robustness.DGANS is made up of two consecutive generative adversarial networks that can hide a grayscale image into another color or grayscale image of the same size and can restore it later.The generated stego-images are augmented and used to further train and strengthen the reveal network so as to make it adaptive to small geometric distortion of input images.Experimental results suggest that DGANS can not only realize high-capacity image steganography,but also can resist geometric attacks within certain range,which demonstrates better robustness than similar models.     

Virtual access network embedding in wireless mesh networks

Network virtualization of a wireless mesh network (WMN) is an economical way for different subscribers to customize their exclusive access networks through a common network infrastructure. The most critical task of network virtualization is virtual network embedding, which can be divided into two sub-problems: node mapping and link mapping. Although there exist approaches to virtual network embedding in wired networks, the characteristics of WMNs make virtual network embedding become a unique and challenging problem. In this paper, virtual access network embedding is studied for WMNs. To support flexible resource allocation in virtual access network embedding, each access node is designed based on orthogonal frequency division multiple access (OFDMA) dual-radio architecture. Through subcarrier allocation on each link, virtual access networks are gracefully separated from each other. To coordinate channel assignment across different links under the constraint of a limited number of orthogonal channels, a novel channel allocation algorithm is proposed to exploit partially-overlapped channels to improve resource utilization. Since the virtual access network embedding problem is NP-hard, a heuristic algorithm is developed based on an enhanced genetic algorithm to obtain an approximate but effective solution. Simulation results illustrate that the virtual access network embedding framework developed in this paper works effectively in WMNs.     

一种基于JPEG图像的密写算法

目前,在基于JPEG图像的数字密写中,在保证隐蔽性和信息隐藏量的前提下,鲁棒性往往不能保证,导致提取秘密信息的正确率不够高。针对此,提出将汉明码应用于密写嵌入过程中,利用JPEG图像压缩原理,将处理后的隐蔽信息通过改变DCT系数来表示。通过实验分析与F4密写算法的比较,结果显示该方法具有较好的隐蔽性且具有较好的鲁棒性。     

A novel haze image steganography method via cover-source switching

In realistic outdoor scenarios, image sensors tend to suffer from various weather conditions (e.g., haze, rain, etc.),which make the images of the same scene taken at different times may be different. Therefore, one should be able to securely embed secret messages into these images by making use of the variations of the weather effects. Inspired by some recent natural steganography algorithms, this paper presents a novel haze image steganography method, which embeds messages through adjusting the weather effects of an input haze image, making it resemble the same image captured under another weather condition. The proposed steganography method consists of three parts: (1) model parameter estimation of the input haze image, (2) haze effects adjustment according to the atmospheric scattering model, (3) message embedding using the floating-point adjusted haze image. 10,000 haze images captured under different haze conditions in various scenarios were used to test the proposed steganography algorithm. The experimental results show that the proposed steganography algorithm is more secure than S-UNIWARD and HILL for steganalyzers who only have raw haze images.     

Data Masking: A New Approach for Steganography?

It is well known that encryption provides secure channels for communicating entities. However, due to lack of covertness on these channels, an eavesdropper can identify encrypted streams through statistical tests and capture them for further cryptanalysis. Hence, the communicating entities can use steganography to achieve covertness. In this paper we propose a new form of multimedia steganography called data masking. Instead of embedding a secret message into a multimedia object, as in traditional multimedia steganography, we process the entire secret message to make it appear statistically similar to a multimedia object itself. Thereby we foil an eavesdropper who is primarily applying statistical tests to detect encrypted communication channels. We show that our approach can potentially give a covert channel capacity which is an order of magnitude higher than traditional steganography. Our experiments also show that steganalyzers trained with stego objects of known steganographic have a low detection accuracy for datamasked multimedia objects.     

针对MME的JPEG图像隐写分析方法研究

MME（Modified Matrix Encoding）作为一种新兴的JPEG图像隐写技术,具有隐写量大、对图片的改动小、抗检测性优于传统隐写算法等优点。文中在采用Markov特征的基础上,加入了能够更好描述图像局部纹理的局部二值模式（LBP）与局部顺序对比模式（LOCP）特征,并应用Ensemble分类器完成分类和识别。通过对UCID图像库的大量实验,得到了一种针对MME算法的最优特征组合。相比传统的隐写分析方法,文中所提出的方法具有更好的检测正确率。     

基于Canny边缘检测的自适应空域隐写术

针对自适应空域隐写术设计的关键问题,该文结合Canny边缘检测和校验格编码(STC)提出一种不需要同步边信息的自适应空域隐写方法。首先,根据秘密消息长度、载体图像等因素确定Canny边缘检测算法中的参数取值,进而根据相应的参数取值使用Canny边缘检测算法来选择载体图像的边缘区域。然后,分别定义边缘区域像素和非边缘区域像素的嵌入失真;最后,在载体像素的多个最低有效位平面(LSB)使用STC嵌入秘密消息。实验结果表明:该隐写方法在4种嵌入率情况下抵抗常见通用隐写分析的性能优于3种已有的隐写方法,且在较小嵌入率情况下与空域通用小波相对失真方法(S-UNIWARD)相当。     

安全的密文域图像隐写术

基于同态加密和双层隐写编码,该文提出一种安全的密文域图像隐写术,其可以达到传统明文隐写术的容量,并且在密文域和明文域均能有效抵抗隐写检测分析。首先结合自适应隐写术和湿纸编码技术,提出一种明文域双层隐写算法;其次,修正一种全同态加密算法,对载密图像进行加密;最后,在密文域上提取嵌入的信息。理论分析和实验结果表明:在加密/隐写密钥同时泄露、加密密钥泄露和密钥未泄露条件下,算法均具有较高的安全性。     

Fast coding in digital steganography

Dynamical running coding was previously proposed as a method for construction of steganographic schemes in digital steganography. In this article, a new approach of fast coding is proposed to improve steganographic efficiency. Original embedding method is discussed in two cases of constructing different generating matrix; however, the proposed method generalizes the problem to only one case by changing the construction method from the original method. Therefore, the complexity of the proposed method is less than the original method. Meanwhile, both theoretical analysis and experiment results show better performance in terms of distortions and alterations of the cover objects when embedding the same amount of information data bits, which proves a better resistance against steganalysis.     

基于遗传优化图像稀疏分解的密写算法

根据超完备字典图像稀疏表示的稀疏性和特征保持性,提出了基于遗传优化图像稀疏分解的密写算法。该密写算法将信息隐藏与基于图像稀疏分解的压缩过程合二为一。首先在基于MP的图像稀疏分解每步迭代中,采用遗传算法快速实现最佳匹配原子的选取;对稀疏分解得到的结果用不同的量化位数进行量化;最后采用LSB嵌入方式将秘密信息隐藏于量化后参数的不同最低有效位中,得到载密图像。实验结果表明,本文提出的基于遗传优化图像稀疏分解的密写算法具有良好的视觉效果, 与相同嵌入容量的经典空域和DCT域LSB算法相比,本文的密写算法获得了更高的抵抗隐写分析能力。抗隐写分析实验也表明新的密写算法对嵌入位数不敏感,可灵活地扩充嵌入容量。      

图像插值空间完全可逆可分离密文域信息隐藏算法

密文域可逆信息隐藏技术在医学、云服务、军事、商业等众多领域有着广泛应用,针对现有密文域信息隐藏算法的可逆性不能完全保证、嵌入率低、不能完全分离等不足,提出一种完全可逆可分离密文域信息隐藏算法,首先,给出了适合图像加密遍历矩阵所需满足的条件和构造方法,载体图像拥有者设置密钥1构造遍历矩阵,并对明文图像进行加密,然后将加密图像传送给信息嵌入者,信息嵌入者设置密钥2,以期望插值为目标,根据插值区间大小确定嵌入位数,再由差值修正因子和秘密信息共同确定最终插值,使最终插值最大限度接近期望插值,确保载密图像高质量,整个过程无附加信息、无数据溢出、且均可保证可逆性,密钥1拥有者和密钥2拥有者两种权限互不干涉,是完全可逆可分离算法,平均嵌入率可达到3 bit/pixel,通过与8种优秀算法的实验比较,表明算法在嵌入容量、可逆性、可分离性率等方面相比于对比算法均有一定优势.     

Steganalysis of Low Embedding Rates LSB Speech Based on Histogram Moments in Frequency Domain

As wavelet packet transform is able to focus on minute change of signals, this study proposes an analytic approach of low embedding steganograpy based on high order Histogram moments in frequency domain (HMFD), which provides a key solution to the feature selection and extraction of HMFD. The detection results are tested with the LSB matching steganograpy of different embedding rates in speech signals, respectively, it is proved that the detection performance with HMFD applied is greater than that of histogram statistical moments. HMFD by Wavelet packet decomposition (WPD) can effectively detect low embedding rates Least significant bit (LSB) speech steganography, its accuracy can be 60.8％ while the embedding rate is only 3％.     

基于多层感知卷积和通道加权的图像隐写检测

针对目前图像隐写检测模型中线性卷积层对高阶特征表达能力有限,以及各通道特征图没有区分的问题,该文构建了一个基于多层感知卷积和通道加权的卷积神经网络(CNN)隐写检测模型。该模型使用多层感知卷积(Mlpconv)代替传统的线性卷积,增强隐写检测模型对高阶特征的表达能力;同时引入通道加权模块,实现根据全局信息对每个卷积通道赋予不同的权重,增强有用特征并抑制无用特征,增强模型提取检测特征的质量。实验结果表明,该检测模型针对不同典型隐写算法及不同嵌入率,相比Xu-Net, Yedroudj-Net, Zhang-Net均有更高的检测准确率,与最优的Zhu-Net相比,准确率提高1.95%～6.15%。