Hiding data in images by simple LSB substitution

In this paper, a data hiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego-image obtained by the simple LSB substitution method, the image quality of the stego-image can be greatly improved with low extra computational complexity. The worst case mean-square-error between the stego-image and the cover-image is derived. Experimental results show that the stego-image is visually indistinguishable from the original cover-image. The obtained results also show a significant improvement with respect to a previous work.     

A novel lossless data compression scheme based on the error correcting Hamming codes

This paper introduces a novel lossless binary data compression scheme that is based on the error correcting Hamming codes, namely the HCDC scheme. In this scheme, the binary sequence to be compressed is divided into blocks of n bits length. To utilize the Hamming codes, the block is considered as a Hamming codeword that consists of p parity bits and d data bits (n=d+p). Then each block is tested to find if it is a valid or a non-valid Hamming codeword. For a valid block, only the d data bits preceded by 1 are written to the compressed file, while for a non-valid block all n bits preceded by 0 are written to the compressed file. These additional 1 and 0 bits are used to distinguish the valid and the non-valid blocks during the decompression process. An analytical formula is derived for computing the compression ratio as a function of block size, and fraction of valid data blocks in the sequence. The performance of the HCDC scheme is analyzed, and the results obtained are presented in tables and graphs. Finally, conclusions and recommendations for future works are pointed out.     

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 lossless robust data hiding scheme

This paper presents a lossless robust data hiding scheme. The original cover image can be recovered without any distortion after the hidden data have been extracted if the stego-image remains intact, and on the other hand, the hidden data can still be extracted correctly if the stego-image goes through JPEG compression to some extent. The proposed scheme divides a cover image into a number of non-overlapping blocks and calculates the arithmetic difference of each block. Bits are embedded into blocks by shifting the arithmetic difference values. The shift quantity and shifting rule are fixed for all blocks, and reversibility is achieved. Furthermore, owing to the separation of bit-0-zone and bit-1-zone as well as the particularity of arithmetic difference, minor alteration applying to the stego-image generated by non-malicious attacks such as JPEG compression will not cause the bit-0-zone and the bit-1-zone to overlap, and robustness is achieved. Experimental results show that, compared with previous works, the performance of the proposed scheme is significantly improved.     

Adaptive Data Hiding,Using Pixel-Value-Differencing and LSB Substitution

With the development of Internet communications, the security of message sending on the Internet has become very important. This article proposes a new adaptive data hiding method with a large data-embedding capacity for gray-scale images to raise the security of sending a message between sender and receiver in networks. At first, the image is divided into some blocks consisting of two consecutive pixels. If the values of both pixels are small, fewer secret bits will be embedded within the two pixels, otherwise, the difference value of two pixels is calculated, and according to the obtained difference value, the method will estimate the number of embedding bits into LSBs of two pixels. This number is adaptive and depends on the range to which the difference value belongs. A readjusting phase is presented to keep the difference of value pixels in the same range before and after embedding. Experimental results show that our method has increased the capacity of embedding bits in comparison with the several other methods.     

一种关于LSB matching的隐写分析方法的改进

LSB matching是一种广泛使用的数字图像隐写方法,但关于其隐写分析的工作目前还不多。详细讨论了最近提出的一种基于图像直方图局部极值点的关于LSB matching的隐写分析方法,指出了该方法的不足,并结合图像采样技术给出了一种新的用于LSB matching的检测子。实验表明,提出的检测子实现简单并且具有更好的检测效率。     

A path optional lossless data hiding scheme based on VQ joint neighboring coding

Data hiding is an important technique for covert communication that embeds secret data into a cover image with minimal perceptible degradation. Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but the cover media can be reconstructed without any distortion. In this paper, a novel path optional lossless data hiding scheme based on the joint neighboring coding (JNC) of the vector quantization (VQ) index table is proposed. The proposed scheme generates a VQ index table based on the cover image first. Next, according to an initial key and secret data content, different adjacent indices may be chosen to perform joint neighboring coding for each index and hide secret data. Finally, an appropriate output codestream is generated based on the minimal length principle. Our main contributions lie in three aspects: (1) the method combines the novel path_based shift method with the traditional JNC method to improve the capacity and stego image quality simultaneously. (2) The secret data extraction and cover image recovering processes are separated and both can be publicized to users. (3) The method is path optional to meet various users’ requirements. To testify the superiority of the proposed method, we compare it with the side match VQ (SMVQ)-based and modified fast correlation VQ (MFCVQ)-based algorithms. According to the experimental results, the proposed scheme outperforms the SMVQ-based and MFCVQ-based algorithms in four aspects, i.e., hiding capacity, stego image quality, transmission efficiency and security level.     

一种基于LSB和PVD的图像信息隐藏方法研究

为了提高图像中秘密信息的嵌入量,提出并实现了一种基于最低有效位和像素值差异的图像信息隐藏方法。通过计算两个连续像素值的差异来判断图像的一致区域和边界区域,在一致区域使用最低有效位方法,而在边界区域使用像素值差异方法。实验结果表明,结合这两种方法后信息的嵌入量是单纯使用像素值差异方法的1.59～1.97倍,并且隐写图像具有大于38 dB的峰值信噪比,保证了隐写图像的视觉质量。     

一种改进的LSB数字图像隐藏算法*

目前针对常用的LSB信息隐藏,SPA分析(sample pair analysis)和RS方法(regularand singular groups method)能以很高的精度估计出图像中隐藏信息的比率。基于几何变换的性质,提出了一种可用于图像置乱技术的亚仿射变换,并利用矩阵编码思想改进LSB的嵌入方式,从而使嵌入数据获得了较好的抗隐写分析性能。实验结果表明,该算法能有效抵抗RS和SPA隐写分析,并保持图像的直方图统计特征,且适用于灰度图像和彩色图像,易于实现。     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

抗统计分析的LSB密写方案

由于通过RS统计分析和Chi—square统计分析，可以察觉以LSB方法密写的秘密信息的存在，因此为提高密写方案的安全性，提出了一种改进的LSB密写方案，即如果被嵌入的秘密比特与原始灰度的最低位相同，便不作改动；否则根据周围像素作增1或减1的调整，而在接收方，只需将载体图象的最低位取出即可恢复秘密信息．大量图象的模拟实验结果说明，该方案不仅可以抵抗RS分析和Chi—square分析，而且不增加失真度．并可保持计算量小、提取方便的优点．并表明改进方案对抵抗这两种密写分析是有效的．     

An encoding method for both image compression and data lossless information hiding

Reversible image data hiding technology means the cover image can be totally recovered after the embedded secret data is extracted. In this paper, we propose a reversible image data hiding scheme based on vector quantization (VQ) compressed images. The secret bits are embedded into the VQ index table by modifying the index value according to the difference of neighboring indices. The data hiding capacity and the size of the final codestream (embedded result) are a trade-off, and it can be decided by users. In other words, the proposed scheme has flexible hiding capacity ability. To estimate the performance, the proposed scheme was compared with the scheme proposed by Wang and Lu (2009). The results of the comparison showed that our proposed scheme is superior to the scheme proposed by Wang and Lu in both data hiding capacity and bit rate.     

Lossless data hiding using bit-depth embedding for JPEG2000 compressed bit-stream

We have proposed a reversible information hiding for binary images. In this paper, we present a Iossless data hiding method for JPEG2000 compressed data based on the reversible information hiding. In JPEG2000 compression, wavelet coefficients of an image are quantized, therefore, the least significant bit plane （LSB） can be extracted. The proposed method recovers the quantized wavelet coefficients of cover images from Stego images. To realize this, we embed not only secret data and the JBIG2 bit-stream of a part of the LSB plane but also the bit-depth of the quantized coefficients on some code-blocks. Experimental results demonstrate the feasibility of application of the proposed method to image alteration detection for JPEG2000 compressed data.     

关于空间数据系统无损压缩算法的零值块研究

通过对CCSDS(国际空间数据系统咨询委员会)建议的无损数据压缩标准的研究,以及对目前常用压缩算法的调查,它阐述了一种具有延迟小速度快抗差错能力强等特点的无损数据压缩算法,即Rice压缩算法,压缩率超过50%以上,而且对多种类型的数据都会达到满意的效果.它对算法中的零值块部分作了较为详细地阐述,因为经过预处理过的数据通常都很小,对于图像来说有相当多的零值.因此,对零值较多的情况下采取零值块压缩处理,效果很好,经过软件测试,结果符合CCSDS的要求标准.     

一种适合于传感器网络的新型压缩算法研究

数据压缩可以高效节约网络能量,有效延长网络寿命。针对B-LZW算法的缺陷,提出了一种新型的可以直接应用于传感器网络的改进算法—MC-B-LZW;在设计中引入了miniCache,完善了算法的操作性,可使该算法嵌入到现有的各类传感节点中;测试B-LZW及其改进算法MC8,MC16,MC32和MC64的性能;通过比较压缩率和执行时间2个指标,得出结论:MC16算法性能最优,压缩率较B-LZW平均提高13.6%,执行时间较B-LZW几乎没有延长,是一种比较理想的压缩算法。     

A data mining approach to database compression

Data mining can dig out valuable information from databases to assist a business in approaching knowledge discovery and improving business intelligence. Database stores large structured data. The amount of data increases due to the advanced database technology and extensive use of information systems. Despite the price drop of storage devices, it is still important to develop efficient techniques for database compression. This paper develops a database compression method by eliminating redundant data, which often exist in transaction database. The proposed approach uses a data mining structure to extract association rules from a database. Redundant data will then be replaced by means of compression rules. A heuristic method is designed to resolve the conflicts of the compression rules. To prove its efficiency and effectiveness, the proposed approach is compared with two other database compression methods. Chin-Feng Lee is an associate professor with the Department of Information Management at Chaoyang University of Technology, Taiwan, R.O.C. She received her M.S. and Ph.D. degrees in 1994 and 1998, respectively, from the Department of Computer Science and Information Engineering at National Chung Cheng University. Her current research interests include database design, image processing and data mining techniques. S. Wesley Changchien is a professor with the Institute of Electronic Commerce at National Chung-Hsing University, Taiwan, R.O.C. He received a BS degree in Mechanical Engineering (1989) and completed his MS (1993) and Ph.D. (1996) degrees in Industrial Engineering at State University of New York at Buffalo, USA. His current research interests include electronic commerce, internet/database marketing, knowledge management, data mining, and decision support systems. Jau-Ji Shen received his Ph.D. degree in Information Engineering and Computer Science from National Taiwan University at Taipei, Taiwan in 1988. From 1988 to 1994, he was the leader of the software group in Institute of Aeronautic, Chung-Sung Institute of Science and Technology. He is currently an associate professor of information management department in the National Chung Hsing University at Taichung. His research areas focus on the digital multimedia, database and information security. His current research areas focus on data engineering, database techniques and information security. Wei-Tse Wang received the B.A. (2001) and M.B.A (2003) degrees in Information Management at Chaoyang University of Technology, Taiwan, R.O.C. His research interests include data mining, XML, and database compression.     

统计量移位的鲁棒无损图像信息隐藏

鲁棒无损信息隐藏在医学成像、法律取证、遥感等领域有广泛的应用。提出了一种鲁棒无损图像信息隐藏算法。在含密载体图像未受损情况下,正确提取秘密信息后可无损恢复原始载体图像;在含密载体图像受到一定程度JPEG2000压缩攻击后,秘密信息仍然可以被正确提取。首先将原始载体图像分块并计算每个图像块的统计量,再根据统计量绝对值的最大值选择合适的阈值对统计量进行移位,最后利用移位后的统计量来嵌入秘密信息。实验结果表明,该算法在图像视觉质量、嵌入容量和鲁棒性3个方面都具有很好的性能。与其他鲁棒无损嵌入方法相比,在图像视觉质量和鲁棒性大致相当的情况下,该算法的嵌入容量有了很大提高,表明该算法较其他算法具有明显的优势。     

一种新的无损图像压缩算法的研究

提出了新的二进制(位级)无损图像压缩方法——将错误纠正BCH码引入到图像压缩算法中;将图像的二进制分为大小为7的码字,这些块进入到BCH解码器,消除了校验位后,使得原来的块的大小减少到4位。实验结果表明,此压缩算法是有效的,并给出了一个很好的压缩比,而且不丢失数据。BCH码的使用在提高压缩比方面比单纯霍夫曼压缩的结果要好。     

基于预测误差差值扩展和最低有效位替换的可逆数据隐藏

将差值扩展技术应用于彩色图像,提出一种基于预测误差差值扩展和最低有效位（LSB）替换的彩色图像可逆数据隐藏算法。针对传统差值扩展技术存在过分修改像素灰度值、定位图偏大等缺点,首先利用色彩分量间的相关性减小差值,并将差值扩展量分散到两个色彩分量中;其次,改变差值扩展公式以减少不可扩展差值的数量,提高定位图的压缩率,从而增加嵌入容量;最后,运用LSB替换法嵌入数据,将差值扩展与数据嵌入过程分离,嵌入端和提取端均只需进行一次差值扩展,嵌入和提取效率得以提高。提取端在提取信息时可根据需要无损地恢复原始图像。实验结果表明,该算法在提高嵌入容量和图像质量的同时降低了算法复杂度。