High-capacity image hiding scheme based on vector quantization

A novel grayscale image hiding scheme that is capable of hiding multiple secret images into a host image of the same size is proposed in this paper. The secret images to be hidden are first compressed by vector quantization with additional index compression process. Then, the compressed secret images are encrypted and embedded into the least-significant bits of the host pixels. To provide good image quality of the stego-image, the modulus function and the image property are employed to hide the secret bits into the host pixels and determine the number of hidden bits in each host pixel, respectively. According to the results, the proposed scheme provides a higher hiding capacity and a higher degree of security than that of the virtual image cryptosystem.     

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.     

Lossless compression and information hiding in images

This paper presents an image lossless compression and information-hiding schemes based on the same methodology. The methodology presented here is based on the known SCAN formal language for data accessing and processing. In particular, the compression part produces a lossless compression ratio of 1.88 for the standard Lenna, while the hiding part is able to embeds digital information at 12.5% of the size of the original image. Results from various images are also provided.     

Search-order coding method with indicator-elimination property

Vector quantization (VQ) is a widely used technique for many applications especially for lossy image compression. Since VQ significantly reduces the size of a digital image, it can save the costs of storage space and image delivery. Search-order coding (SOC) was proposed for improving the performance of VQ in terms of compression rate. However, SOC requires extra data (i.e. indicators) to indicate source of codewords so the compression rate may be affected. To overcome such a drawback, in this paper, a search-order coding with the indicator-elimination property was proposed by using a technique of reversible data hiding. The proposed method is the first one using such a concept of data hiding to achieve a better compression rate of SOC. From experimental results, the performance of the SOC method can be successfully improved by the proposed indicator eliminated search-order coding method in terms of compression rate. In addition, compared with other relevant schemes, the proposed method is also more flexible than some existing schemes.     

Reversible steganography based on side match and hit pattern for VQ-compressed images

Data hiding is an important technique in multimedia security. Multimedia data hiding techniques enable message senders to disguise secret data by embedding them into cover media. Thus, delivering secret messages is as easy as sending the cover media. Recently, many researchers have studied reversible data hiding for images. Those methods can reconstruct the original cover image and extract the embedded secret data from a stego-image. This study proposes a novel reversible steganographic method of embedding secret data into a vector quantization (VQ) compressed image by applying the concept of side match. The proposed method uses extra information, namely the hit pattern, to achieve reversibility. Moreover, its small hit pattern enables the embedding of the entire hit pattern along with the secret data in most cases. To optimize visual quality of the output stego-image, the method applies the concept of partitioned codebooks (state codebooks). The partition operation on the codebook uses a look-up table to minimize embedding and extraction time. We also propose the use of diagonal seed blocks to embed the entire hit pattern into the cover image without producing any extra control messages. Compared to the Chang and Lin method, the experimental results show that the proposed method has higher capacity, better visual quality, and shorter execution time.     

Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding

An efficient nearest neighbor codeword search algorithm for vector quantization based on the Hadamard transform is presented in this paper. Four elimination criteria are derived from two important inequalities based on three characteristic values in the Hadamard transform domain. Before the encoding process, the Hadamard transform is performed on all the codewords in the codebook and then the transformed codewords are sorted in the ascending order of their first elements. During the encoding process, firstly the Hadamard transform is applied to the input vector and its characteristic values are calculated; secondly, the codeword search is initialized with the codeword whose Hadamard-transformed first element is nearest to that of the input vector; and finally the closest codeword is found by an up-and-down search procedure using the four elimination criteria. Experimental results demonstrate that the proposed algorithm is much more efficient than the most existing nearest neighbor codeword search algorithms in the case of problems of high dimensionality.     

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.     

矢量量化压缩图像中的安全隐写方法

提出一种用于矢量量化压缩图像的安全数据隐藏方案.为降低数据嵌入引入的失真,以码字间的矢量均方差为优化指标,采用遗传算法实现码本的优化分割,并提出基于码本分割的数据嵌入算法.采用基于自适应算术熵解码的数据映射方法,实现了嵌入前后统计特性的保持.实验结果表明,所提出的算法在容量、失真水平和安全性方面具有较好的综合性能.     

基于矢量量化的数字视频压缩技术

对基于矢量量化的数字视频压缩技术进行了论述，并根据分析结果提出了一种新的矢量量化算法——多级矢量量化联合编码算法，该算法保持了多级矢量量化算法编码速度快、图像相关性小等特点，同时提高了编码质量。实验表明，该算法与已有的算法相比具有较高的图像质量和更高的压缩率。     

Low bit-rate information hiding method based on search-order-coding technique

Information hiding method with low bit rate is important in secure communications. To reduce bit rate we propose a new embedding method in this paper based on SOC (search-order coding) compression technique. Compared to Chang et al.’s scheme in 2004, our scheme completely avoids the transform from SOC coding to OIV (original index values) coding to significantly reduce bit rate. In order to further reduce bit rate, Chang et al. proposed a reversible data hiding scheme using hybrid encoding strategies by introducing the side-match vector quantization (SMVQ) in 2013. But it needed additional 1 bit indicator to distinguish the two statuses to determine OIV is belonged to G1 or G2. This overhead gave a large burden to compression rate and could not reduce the bit rate significantly. In contrast, our scheme completely avoids this indicator. The experimental results show that the proposed method can efficiently reduce the bit rate and have the same embedding capacity compared with Chang et al.’s scheme in 2004 and Chang et al.’s scheme in 2013. Moreover, our proposed scheme can also achieve a better performance in both the embedding capacity and bit rate than other related VQ-based information hiding schemes.     

Multilevel reversible data hiding based on histogram modification of difference images

Reversible data hiding has drawn considerable attention in recent years. Reversibility allows original media to be completely recovered from marked media without distortion after embedded message has been extracted. In this paper we propose a multilevel reversible data hiding scheme based on the difference image histogram modification that uses the peak point to hide messages. Through a joint imperceptibility and hiding capacity evaluation, we show that our proposed scheme uses a multilevel hiding strategy to achieve large hiding capacity and keep distortion low. Performance comparisons with other existing reversible hiding schemes are provided to demonstrate the validity of our proposed scheme.     

Reversible hiding in DCT-based compressed images

This paper presents a lossless and reversible steganography scheme for hiding secret data in each block of quantized discrete cosine transformation (DCT) coefficients in JPEG images. In this scheme, the two successive zero coefficients of the medium-frequency components in each block are used to hide the secret data. Furthermore, the scheme modifies the quantization table to maintain the quality of the stego-image. Experimental results also confirm that the proposed scheme can provide expected acceptable image quality of stego-images and successfully achieve reversibility.     

Data hiding methods based upon DNA sequences

In this paper, three data hiding methods are proposed, based upon properties of DNA sequences. It is highlighted that DNA sequences possess some interesting properties which can be utilized to hide data. These three methods are: the Insertion Method, the Complementary Pair Method and the Substitution Method. For each method, a reference DNA sequence S is selected and the secret message M is incorporated into it so that S^′ is obtained. S^′ is then sent to the receiver and the receiver is able to identify and extract the message M hidden in S^′. Furthermore, the robustness and the tightly embedded capacity analysis of the three proposed methods are demonstrated. Finally, experimental results indicate a better performance of the proposed methods compared to the performance of the competing methods with respect to several parameters such as capacity, payload and bpn.     

Image indexing and retrieval based on vector quantization

To effectively utilize information stored in a digital image library, effective image indexing and retrieval techniques are essential. This paper proposes an image indexing and retrieval technique based on the compressed image data using vector quantization (VQ). By harnessing the characteristics of VQ, the proposed technique is able to capture the spatial relationships of pixels when indexing the image. Experimental results illustrate the robustness of the proposed technique and also show that its retrieval performance is higher compared with existing color-based techniques.     

Data hiding in grayscale images by dynamic programming based on a human visual model

A new method for data hiding in grayscale images based on a human vision model with distortion-minimizing capabilities is proposed. Each of the eight bit planes of an input grayscale image is viewed as a binary image, into which message data are embedded horizontally. Two optimization techniques, namely, block pattern coding and dynamic programming, are proposed for image distortion minimization. Experimental results show good performs of the proposed method.     

An embedding technique based upon block prediction

This paper presents a novel data hiding scheme for VQ compression images. This scheme first uses SMVQ prediction to classify encoding blocks into different types, then uses different codebooks and encoding strategies to perform encoding and data hiding simultaneously. In using SMVQ prediction, no extra data is required to identify the combination of encoding strategies and codebook, which helps improve compression performance. Furthermore, the proposed scheme adaptively combines VQ and SMVQ encoding characteristics to provide higher image quality of stego-images while size of the hidden payload remains the same. Experimental results show that the proposed scheme indeed outperforms other previously proposed schemes in image quality of the stego-images and compression performance.     

High capacity data hiding schemes for medical images based on difference expansion

Since the difference expansion (DE) technique was proposed, many researchers tried to, improve its performance in terms of hiding capacity and visual quality. In this paper, a new scheme, based on DE is proposed in order to increase the hiding capacity for medical images. One of the characteristics of medical images, among the other types of images, is the large smooth regions. Taking advantage of this characteristic, our scheme divides the image into two regions; smooth region and non-smooth region. For the smooth region, a high embedding capacity scheme is applied, while the original DE method is applied to the non-smooth region. Sixteen DICOM images of different modalities were used for testing the proposed schemes. The results showed that the proposed scheme has higher hiding capacity compared to the original schemes.     

A high capacity data hiding scheme for binary images based on block patterns

This paper proposes a high capacity data hiding scheme for binary images based on block patterns, which can facilitate the authentication and annotation of scanned images. The scheme proposes block patterns for a 2 × 2 block to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. In addition, two kinds of matching pair (MP) methods, internal adjustment MP and external adjustment MP, are designed to decrease the embedding changes. Shuffling is applied before embedding to reduce the distortion and improve the security. Experimental results show that the proposed scheme gives a significantly improved embedding capacity than previous approaches in the same level of embedding distortion. We also analyze the perceptual impact and discuss the robustness and security issues.     

An adaptive data hiding scheme with high embedding capacity and visual image quality based on SMVQ prediction through classification codebooks

This study exploits the characteristics of image blocks to develop an adaptive data hiding scheme that is based on SMVQ prediction. Since human beings’ eyes are highly sensitive to smooth images, changes in smooth cause great distortion and attract the attention of interceptors. Hence, this study proposes a data embedding scheme for embedding secret data into edge blocks and non-sufficiently smooth blocks. The experimental results show that the proposed scheme improves the quality of the stego-image and the embedding capacity.