On the embedding limits of the discrete cosine transform

This paper investigates the embedding capacity limits of high-capacity data hiding in color images based on a locally Adaptive-Region Discrete Cosine Transform (AR-DCT) frequency domain data hiding scheme, and explores the relationship between hiding capacity and image quality. It also compares the embedding capacities of various steganography schemes which have been recently published in the literature. Experimental results confirm that our proposed scheme successfully enhances hiding capacity while maintaining acceptable image quality and concludes that the capacity for our DCT hiding scheme can achieve extremely high bit rates of 20 bits-per-pixel, which is much higher than other DCT-based approaches, as well as other spatial and frequency domain schemes.     

Performance enhanced image steganography systems using transforms and optimization techniques

Image steganography is the art of hiding highly sensitive information onto the cover image. An ideal approach to image steganography must satisfy two factors: high quality of stego image and high embedding capacity. Conventionally, transform based techniques are widely preferred for these applications. The commonly used transforms for steganography applications are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) etc. In this work, frequency domain transforms such as Fresnelet Transform (FT) and Contourlet Transform (CT) are used for the data hiding process. The secret data is normally hidden in the coefficients of these transforms. However, data hiding in transform coefficients yield less accurate results since the coefficients used for data hiding are selected randomly. Hence, in this work, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used for improving the performance of the steganography system. GA and PSO are used to find the best coefficients in order to hide the Quick Response (QR) coded secret data. This approach yields an average PSNR of 52.56 dB and an embedding capacity of 902,136 bits. These experimental results validate the practical feasibility of the proposed methodology for security applications.     

A new steganography algorithm using polar decomposition

Nowadays, the security of communication becomes very important with the rapid development of network technology. So, the transmission and distribution of the several digital information must be protected and secured against other users. Many steganography techniques have been proposed for embedding secret digital data in other digital data. In this article, we propose a new steganography algorithm based on a linear algebraic tool that is the polar decomposition (PD) for hiding secret data in an image. A host image is selected and divided into blocks of size 2 × 2, a PD is applied on each block, and the secret data are embedded in suitable blocks. Experimental results show that our proposed algorithm gives a higher hiding capacity, achieves good imperceptibility, and also provides a high degree of security against common types of attacks such as compression attack with quality 10%, gamma correction attack, and impulse noise attack.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.     

A more secure steganography based on adaptive pixel-value differencing scheme

Pixel-value differencing (PVD) based steganography is one of popular approaches for secret data hiding in the spatial domain. However, based on extensive experiments, we find that some statistical artifacts will be inevitably introduced even with a low embedding capacity in most existing PVD-based algorithms. In this paper, we first analyze the common limitations of the original PVD and its modified versions, and then propose a more secure steganography based on a content adaptive scheme. In our method, a cover image is first partitioned into small squares. Each square is then rotated by a random degree of 0, 90, 180 or 270. The resulting image is then divided into non-overlapping embedding units with three consecutive pixels, and the middle one is used for data embedding. The number of embedded bits is dependent on the differences among the three pixels. To preserve the local statistical features, the sort order of the three pixel values will remain the same after data hiding. Furthermore, the new method can first use sharper edge regions for data hiding adaptively, while preserving other smoother regions by adjusting a parameter. The experimental results evaluated on a large image database show that our method achieves much better security compared with the previous PVD-based methods.     

LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.     

一种DCT域稳健的彩色图像隐藏方法

基于YCbCr色彩系统,提出了一种稳健的DCT域彩色图像隐藏方法,可以将一幅彩色图像隐藏在另一幅公开的彩色载体图像中,并与JPEG压缩标准相兼容。在嵌入过程中,通过对彩色图像各分量值进行合理分配和标志性嵌入,可以有效地克服DCT域秘密图像提取时的严重失真问题;并根据HVS特征和模糊理论对DCT图像块进行模糊分类,实现嵌入强度的自适应变化。实验结果表明,提出的方法对嵌入过程中产生的噪声具有很强的稳健性,恢复的秘密图像的像素值最大失真误差在±1之内,在保证隐秘图像视觉质量的前提下,载体图像具有较大的数据隐藏量。     

Secure steganography based on embedding capacity

Mostly the embedding capacity of steganography methods is assessed in non-zero DCT coefficients. Due to unequal distribution of non-zero DCT coefficients in images with different contents, images with the same number of non-zero DCT coefficients may have different actual embedding capacities. This paper introduces embedding capacity as a property of images in the presence of multiple steganalyzers, and discusses a method for computing embedding capacity of cover images. Using the capacity constraint, embedding can be done more secure than the state when the embedder does not know how much data can be hidden securely in an image. In our proposed approach, an ensemble system that uses different steganalyzer units determines the security limits for embedding in cover images. In this system, each steganalyzer unit is formed by a combination of multiple steganalyzers from the same type, which are sensitive to different payloads. The confidence of each steganalyzer on an image leads us to determine the upper bound of embedding rate for the image. Considering embedding capacity, the steganographer can minimize the risk of detection by selecting a proper cover image that is secure for a certain payload. Moreover, we analyzed the relation between complexity and embedding capacity of images. Experimental results showed the effectiveness of the proposed approach in enhancing the security of stego images.     

高清晰图象水印算法

提出了一个针对图象水印的DCT域的水印算法,该算法基于利用人眼视觉掩蔽特性(HVS)的块分类方法,对原有的此类方法进行了改进,更大挖掘了载体图象的信息隐藏能力。在图象水印的嵌入方法上,采取了将水印图象进行DCT变换从而可以利用各块比例系数的差异嵌入不同频率的DCT系数。同时,对各块的DCT系数进行调整,使得各频率的能量基本相当,间接地提高了信噪比。经过以上几个方面的改进,得到的水印具有很好的清晰度。     

A data embedding scheme for color images based on genetic algorithm and absolute moment block truncation coding

Recently, embedding a large amount of secret data into gray-level and color images with low distortion has become an important research issue in steganography. In this paper, we propose a data embedding scheme by using a well-known genetic algorithm, block truncation code and modification direction techniques to embed secret data into compression codes of color images to expand the variety of cover media. In the scheme, the common bitmap generation procedure of GA-AMBTC has been modified to speed up the hiding procedure. Two embedding strategies are proposed to hide secret data into the common bitmap and the quantization values in each block of the cover image. Experimental results confirm that the proposed scheme can provide high data capacity with acceptable image quality of the stego-images. Moreover, the compression ratio of the scheme is exactly the same as that of GA-AMBTC so that attackers cannot detect any trace of hidden data from the size of the modified compressed result.     

数字图像鲁棒隐写综述

随着智能设备和社交网络的飞速发展,通过网络传输的数字图像成为了实施隐蔽通信的新型重要载体,适应网络信道的图像隐写技术有望成为开放网络环境下可靠、隐蔽传递信息的一种重要方式。然而,数字图像通过Facebook、Twitter、微信、微博等社交网络传输的过程中,往往会遭受压缩、缩放、滤波等处理,对传统信息隐藏技术在兼顾鲁棒性与抗检测性方面提出了新的挑战。为此,研究者经过多年的努力探索,提出了可抵抗多种图像处理攻击和统计检测的新型鲁棒隐写技术。本文结合网络有损信道中隐蔽通信应用需求,对现有的数字图像鲁棒隐写技术进行综述。首先简要介绍本领域的研究背景,并从图像水印和隐写两方面对图像信息隐藏技术的基本概念、相关技术和发展趋势进行了简要总结。在此基础上,将图像鲁棒隐写技术的研究架构分为载体图像选择、鲁棒载体构造、嵌入代价度量、嵌入通道选择、信源/信道编码以及应用安全策略等方面,并分别对相关方法的基本原理进行了归纳和阐述。随后,对具有代表性的相关方法进行了对比测试,并结合应用场景需求给出了推荐的鲁棒隐写方法。最后,指出了数字图像鲁棒隐写技术有待进一步研究解决的问题。     

基于特征的JPEG图像信息隐藏的隐蔽性评价

在基于图像的信息隐藏研究过程中,人们对信息隐藏性能的评价十分关注。提出了一种基于特征的JPEG图像信息隐藏隐蔽性的评价方法。此方法首先提取信息隐藏前后的JPEG图像的特征,对这些特征处理后进行作差得到特征改变量,然后将得到的多维特征改变量进行主成分分析法综合评价,从而实现对隐藏方法隐蔽性的评价。     

面向网络数据安全的高效图像隐写研究

针对传统图像隐写算法计算消耗时间较长的问题,提出了立方体迷你数独矩阵(CMSM)和两层数据隐写方案.将矩阵及其相应的数据嵌入和提取算法推广到N维迷你数独矩阵(NMSM),并阐述了提高时间效率的机制.通过定义了距离定位函数,可以显著提高多维参考矩阵的嵌入效率.该数据隐写方案具有复杂度高,能有效地提高数据隐写的安全性.实验结果表明,该数据隐写方案不仅能获得更好的图像质量,而且比其他相关方案具有更高的嵌入容量.     

Current status and key issues in image steganography: A survey

Steganography and steganalysis are the prominent research fields in information hiding paradigm. Steganography is the science of invisible communication while steganalysis is the detection of steganography. Steganography means “covered writing” that hides the existence of the message itself. Digital steganography provides potential for private and secure communication that has become the necessity of most of the applications in today’s world. Various multimedia carriers such as audio, text, video, image can act as cover media to carry secret information. In this paper, we have focused only on image steganography. This article provides a review of fundamental concepts, evaluation measures and security aspects of steganography system, various spatial and transform domain embedding schemes. In addition, image quality metrics that can be used for evaluation of stego images and cover selection measures that provide additional security to embedding scheme are also highlighted. Current research trends and directions to improve on existing methods are suggested.     

无损载体和鲁棒代价结合的JPEG图像鲁棒隐写

目的传统隐写技术在实际社交网络信道上难以保护秘密信息的完整性。在社交网络中,图像往往经过有损压缩信道进行传输,从而导致隐蔽通信失效。为了保证经过压缩信道传输的载密图像鲁棒性,设计安全鲁棒的隐蔽通信技术具有实际应用价值。基于最小化图像信息损失,本文提出无损载体和鲁棒代价结合的JPEG图像鲁棒隐写。方法首先,指出构造无损载体能有效维持隐写安全性和鲁棒性的平衡,对经过压缩信道前后的JPEG图像空域像素块进行差分,构造无损载体以确定鲁棒嵌入域;其次,通过对离散余弦变换(discrete cosine transform, DCT)系数进行"±1"操作,并计算空域信息在压缩传输前后的损失,设计衡量DCT系数抗压缩性能的鲁棒代价;同时,验证在低质量因子压缩信道下鲁棒代价更能区分DCT系数的鲁棒能力,最后,利用校验子格编码(syndrome-trellis code, STC),结合无损载体和鲁棒代价对秘密信息进行嵌入。结果实验在BossBase1.01图像库上进行对比实验,相比于传统JPEG隐写技术,构造无损载体作为嵌入域能有效地将信息平均提取错误率降低24.97%,图像的正确提取成功率提高了21...     

Distortion function based on residual blocks for JPEG steganography

Steganography aims to embed additional data into digital media secretly and minimize the distortion caused by data embedding. Up to now, the key point of minimal distortion embedding framework is to design proper distortion function. In this paper, a distortion function for JPEG steganography based on residual blocks is proposed. To obtain less statistical detectability, Residual Block Values (termed as RBVs for short) and quantization steps are both involved in the proposed distortion function. RBVs are exploited to determine the embedding risk caused by modifications in corresponding DCT block while quantization steps are exploited to determine the embedding risk of the selection channel in a block. By implementing the syndrome trellis coding (STC) to embed secret data, the modifications are constrained in hard-to-detect religions. Experiments show that the proposed method performs better than current state-of-the-art methods of JPEG steganography.     

A secure image steganography based on modified matrix encoding using the adaptive region selection technique

This paper introduced an image steganography technique based on modified matrix encoding to enhance the perceptual quality of the stego images. Additionally, more pixel bit-planes are exploited in the data hiding process to improve the embedding capacity. The number of used image layers depends on the size of the given secret message and the texture characteristic of the cover image. The complexity of the pixel block is identified by the difference between the middle pixel and its neighbors. By performing the suitable embedding solutions of modified matrix encoding, the complexity is unchanged by the data hiding stage. Therefore, the used image regions could be determined precisely in the extraction process without using any additional information. The experimental results proved that the stego images created by the proposed approach achieved higher security against statistical and visual steganalysis techniques than the previous methods. The improvement of the security against detection by Ensemble Classifier is four times as secure as the best security performance of the existing methods.

     

一种DCT域的大容量图像隐写算法

离散余弦变换（DOT）域隐写适用范围广且安全性高,但其隐藏容量小、难以用于隐藏储存和隐蔽通信等应用场合。为了弥补这一缺陷,提出了一种基于位平面复杂度分割（BPCS）隐写的DCT域大容量隐写算法。实验结果表明,新算法较好地实现了隐写的不可见性,与传统DCT域隐写算法相比,隐藏容量也得到了明显的提高。     

基于DCT域相关性的非对称JPEG隐写

目前用于JPEG隐写的失真代价函数对同一DCT系数的加一与减一操作分配相同的代价值。但由于JPEG图像中DCT域的相关性,加一与减一修改对图像内容的影响不同,因此其对应的代价值也理应不同。文中基于DCT域相关性,提出了一种适用于JPEG隐写的通用代价值优化方法,主要考虑JPEG图像中相邻DCT块相同位置上系数的相关性。利用八邻域块中相同位置的DCT系数求平均的方法给出当前DCT系数的预测值。对现有的JPEG失真代价函数,按照向预测值靠拢的原则区分加一和减一的代价值。经过调整的代价值能引导隐写修改后的DCT系数最大程度地向预测值靠拢,增强DCT域相关性,从而提高隐写的安全性。该方法可以与任何现有的JPEG隐写失真代价函数结合使用。实验表明,所提方法几乎不增加原始算法的时间复杂度,同时能有效提高现有JPEG隐写方法的安全性,可使当前隐写分析工具的测试误差平均提升2.4%。     

基于DCT域共生矩阵的JPEG图像隐写分析

提出一种新的针对JPEG图像的通用隐写分析方法,该方法直接提取DCT系数,利用共生矩阵去挖掘出块中低频系数的相关性,最后形成120维特征并用SVM进行分类。针对4种公认的安全性较高的JPEG类嵌入方法F5、Outguess、MB（未去除分块特性MB1,去除分块特性MB2）,对CorelDraw1096张图库进行了实验,结果表明,该方法的识别率和运算速度明显优于现有算法。