An SMVQ-based reversible data hiding technique exploiting side match distortion

Secure online communication is a necessity in today’s digital world. This paper proposes a novel reversible data hiding technique based on side match vector quantization (SMVQ). The proposed scheme classifies SMVQ indices as Case 1 or 2 based on the value of the first state codeword’s side match distortion (SMD) and a predefined threshold t. The proposed scheme uses this classification to switch between compression codes designed for Cases 1 and 2 SMVQ indices. The length of these compression codes is controlled by the parameter ?. Thus, with the selection of appropriate ? and t values, the proposed scheme achieves good compression, creating spaces to embed secret information. The embedding algorithm can embed n secret bits into each SMVQ index, where n = 1, 2, 3, or 4. The experimental results show that the proposed scheme obtains the embedding rates of 1, 2, 3, or 4 bit per index (bpi) at the average bit rates of 0.340, 0.403, 0.465, or 0.528 bit per pixel (bpp) for the codebook size 256. This improves the performance of recent VQ and SMVQ-based data hiding schemes.     

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.     

Histogram-based reversible data hiding for vector quantisation-compressed images

Reversible data hiding is required and preferable in many applications such as medical diagnosis, military, law enforcement, fine art work and so on. The author proposes to use reversible data hiding applications with a vector quantisation (VQ)-compressed image. The histogram of the prediction VQcompressed image is explored. The prediction VQ encoded image is identical to traditional VQ encoding. The index of prediction encoded VQ images is modified to embed secret data. Furthermore, the VQ images can be completely reconstructed by the recovery procedure. The experimental results show the performance of the proposed method and the efficiency of the embedding, extraction and recovery procedures. In comparison with other VQ-based schemes, the proposed method provides a higher hiding capacity and a better stegoimage quality. Also, the lossless VQ image is recovered     

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.     

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.     

Hiding secret data in images via predictive coding

This paper presents a prediction-based image-hiding scheme that embeds secret data into compression codes during image compression. This scheme employs a two-stage structure: a prediction stage and an entropy coding stage. The secret data is embedded into the difference values of a given image after the prediction stage is performed.According to the experimental results, the image quality is better than Jpeg-Jsteg and its improved scheme (Inform. Sci. 141 (1-2) (2002) 123). The average image quality of the stego-images in the proposed scheme is greater than 50 dB when the hiding capacity is 1 bit per pixel, whereas those values in Jpeg-Jsteg and scheme in Chang et al. (Inform. Sci. 141 (1-2) (2002) 123) are 37.04 and 33.73 dB, respectively. The hiding capacity of the proposed scheme is 65,536 bits when the hiding capacity is 1 bit per pixel, whereas it is 53,248 bits in scheme (Inform. Sci. 141 (1-2) (2002) 123) and less than 3000 bits in Jpeg-Jsteg.     

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.     

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.     

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.     

Secure medical images based on data hiding using a hybrid scheme with the Hamming code,LSB, and OPAP

Some medical accidents in hospitals are related to mismanagement of medical images X-ray and computer tomography (CT). A counterfeit medical image leads to medical accidents. Thus, security of medical images is important for patient safety. Data hiding can help patients avoid mistakes of doctors or nurses who confuse patient X-rays or CT scans. A hybrid data hiding scheme was applied that combined the Hamming code and LSB with an optimal pixel adjustment process algorithm for encoding to conceal hidden patient information. In addition, quality and capacity of stego-images were improved. Experimental results confirmed that the hiding capacity provided by this strategy is greater than for other schemes. Moreover, the image quality of stego-images with the proposed scheme remained more than 50 dB for most medical test images. In this experiment, we verified the proposed steganographic data hiding scheme as RS steganalysis.     

基于改进边缘匹配矢量量化的图象编码算法

边缘匹配（Side-Match）矢量量化器（SMVQ）是有限状态矢量量化器（FSVQ）的一个分支。该量化器适合于对图象块间相关性高的图象进行压缩编码,其优点是在比特率相近 的情况下,编码质量高于传统的穷尺搜索矢量量化编码器,但其缺点是计算量大和比特率固定。本文提出了一种改进的边缘匹配矢量量化器。测试结果表明,该算法是变比特率编码算法,它比边缘匹配矢量量化器的比特率低,编码速度快,编码质量有所提高。     

Data hiding based on compressed VQ indices of images

The VQ-based data hiding technique has not received much attention compared to various spatial domain-based data hiding techniques in digital images. Consequently, a new data hiding scheme, applied in the VQ-compressed domain of cover images, is introduced in this article. To provide more hiding capacity for secret data and to keep an acceptable bit rate for the compressed cover images, the search-order-coding (SOC) algorithm was implemented to compress the VQ indices of the cover images in the process of data hiding. During the process of data hiding, the proposed scheme embeds secret data into the compressed VQ indices of the cover image adaptively, adjusting the bit rate according to the size of the secret data and the compressed cover image. In addition, the hiding process induces no extra coding distortion. Experiments show that the receiver can efficiently receive both the secret data and the compressed cover image simultaneously with an acceptable bit rate. Simulation results also demonstrate that our proposed scheme outperforms earlier proposed methods.     

Joint coding and embedding techniques for multimedia images

Hiding data in digital images is a technique for secret data communication. It involves embedding important data into a cover image in digital form with minimal perceptible degradation. In this paper, we first propose an improved data hiding method for side-match vector quantization (SMVQ) compressed images, which utilizes the codeword-pairing procedure to modulate the index code of each image block. In order to take it a step further and obtain a larger embedding capacity, we remodel the first proposed method into a new one with the power of variable-rate data embedding through the use of a multiple-base notational system. According to the experimental results, the two proposed methods are indeed capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with previous methods. Moreover, our methods also maintain a low compression bit-rate without auxiliary data.     

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.     

An invisible hybrid color image system using spread vector quantization neural networks with penalized FCM

In this paper, an invisible hybrid color image hiding scheme based on spread vector quantization (VQ) neural network with penalized fuzzy c-means (PFCM) clustering technology (named SPFNN) is proposed. The goal is to offer safe exchange of a color stego-image in the internet. In the proposed scheme, the secret color image is first compressed by a spread-unsupervised neural network with PFCM based on interpolative VQ (IVQ), then the block cipher Data Encryption Standard (DES) and the Rivest, Shamir and Adleman (RSA) algorithms are hired to provide the mechanism of a hybrid cryptosystem for secure communication and convenient environment in the internet. In the SPFNN, the penalized fuzzy clustering technology is embedded in a two-dimensional Hopfield neural network in order to generate optimal solutions for IVQ. Then we encrypted color IVQ indices and sorted the codebooks of secret color image information and embedded them into the frequency domain of the cover color image by the Hadamard transform (HT). Our proposed method has two benefits comparing with other data hiding techniques. One is the high security and convenience offered by the hybrid DES and RSA cryptosystems to exchange color image data in the internet. The other benefit is that excellent results can be obtained using our proposed color image compression scheme SPFNN method.     

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.     

A fast vector quantization encoding algorithm based on projection pyramid with Hadamard transformation

Vector quantization (VQ) for image compression requires expensive time to find the closest codevector in the encoding process. In this paper, a fast search algorithm is proposed for projection pyramid vector quantization using a lighter modified distortion with Hadamard transform of the vector. The algorithm uses projection pyramids of the vectors and codevectors after applying Hadamard transform and one elimination criterion based on deviation characteristic values in the Hadamard transform domain to eliminate unlikely codevectors. Experimental results are presented on image block data. These results confirm the effectiveness of the proposed algorithm with the same quality of the image as the full search algorithm.     

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。     

A centroid based vector quantization reversible data hiding technique

In this paper, we propose a reversible data hiding scheme that exploits the centroid formula. Specifically, we use it to define a centroid boundary vector and a centroid state codebook CSCB. Initially, our centroid boundary vectors and CSCBs are the same as the side match vector quantization (SMVQ) algorithm’s boundary vectors and state codebooks SCBs. For each VQ index, the proposed scheme exploits the centroid formula to update its centroid boundary vector and the corresponding CSCB. The updating is coupled with a heuristic to select the best state codebook (i.e., either SCB or CSCB) for each VQ index, which generates a highly compressible distribution of index values. Our experimental results show that the proposed scheme can embed n = 1, 2, 3, and 4 bit per index (bpi) at bit rates of 0.332, 0.394, 0.457, and 0.519 bit per pixel (bpp), respectively, for the main codebook size N = 256. These results confirm that the proposed scheme improves recent VQ and SMVQ based reversible data hiding schemes.

     

小波树结构快速矢量量化编码方法

提出了基于人眼视觉属性和应用小波树结构２快速图象编码的矢量量化图象编码方法，简称为树结构快速矢量量化编码。在分析此方法矢量量化特点之后，设计产生码本的统计方法，并提出了矢量量化编码的快速算法。