Lossless data hiding for absolute moment block truncation coding using histogram modification

This paper presents a lossless data hiding method for an absolute moment block truncation coding (AMBTC) images, which is a compressed grayscale image. It is not easy to hide secret data in an AMBTC-compressed image because it is composed of bit planes. Thus, it is very sensitive to change some pixels. Nevertheless, to improve the hiding capacity, we present an efficient extension of the histogram modification technique by counting the coefficients of the bit planes in each 4 × 4 block. In addition, our proposed scheme has low complexity and achieves a high embedding capacity with the good perceptual quality compared to the prior arts. In an experiment, we verified our proposed data hiding scheme through the comparison with previous methods.     

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.     

面向索引图的大容量信息隐藏方法

嵌入容量是衡量信息隐藏方法的重要指标之一。为了提高嵌入容量,提出一种面向矢量量化索引图邻块编码的信息隐藏改进算法,利用当前块的四邻块的空间相对位置关系和邻块与当前块索引差值的数值范围进行信息隐藏。实验结果表明,改进后的算法能够显著提高嵌入容量,在比特率仅增加5.13%13.10%的情况下可将嵌入容量提高到Chang方法容量的2.113.16倍,嵌入效率由原来平均0.10提高到了0.26。     

Adaptive lossless steganographic scheme with centralized difference expansion

In this paper, a novel adaptive lossless data hiding scheme is presented that is capable of offering greater embedding capacity than the existing schemes. Unlike the fixed hiding capacity each block provides in most of the currently available lossless data hiding approaches, the proposed method utilizes a block-based lossless data embedding algorithm where the quantity of the hidden information each block bears is variable. To both reduce the image distortion and increase the hiding capacity, the payload of each block depends on its cover image complexity. Due to the fact that schemes with difference expansion tend to damage the image quality seriously in the edge areas, in the proposed scheme, smoother areas are chosen to conceal more secret bits. This way, a better balance can be reached between the embedding ratio and the stego-image quality. In addition, when recovered the cover image can came back to its old self to the last bit without any distortion at all. Experimental results, as this paper will show, have demonstrated that the proposed method is capable of hiding more secret data while maintaining imperceptible stego-image quality degradation.     

A lossless data embedding technique by joint neighboring coding

Information hiding methods are currently exploited by many researchers for various applications. Proposing an efficient and feasible information hiding method is valuable. This paper presents a new reversible information hiding method for vector quantization (VQ)-compressed grayscale images by using joint neighboring coding (JNC) technique. The proposed method embeds secret data by using the difference values between the current VQ-compressed index and left or upper neighboring indices. The experimental results show that the proposed method achieves the best visual quality of reconstructed images compared with the two related works. In addition, the proposed method obtains as high embedding capacity as Lin and Chang's method, followed by Yang et al.'s method. As for execution speed, Yang et al.'s method is fastest, followed by the proposed method, and then Lin and Chang's method. With respect to bit rate, the proposed method has a little higher bit rate in comparison with the two related works.     

An Adaptive Two-Stage Edge Detection Scheme for Digital Color Images

An adaptive two-stage edge detection scheme for digital color images is proposed in this paper. In the first stage of this scheme, each three-dimensional color image is reduced to a one-dimensional gray-level image using the moment-preserving thresholding technique. Then, a new edge detection technique based on the block truncation coding scheme is introduced to detect the edge boundary in the second stage. The edge detection process makes use of the bit plane information of each BTC-encoded block to detect the edge boundary. The experimental results show that the performance of the detected edge image of the proposed scheme is as good as in Yang's scheme and in the Sobel operator. However, the computational cost consumed by the proposed scheme is less than that of Yang's scheme. In addition, the proposed scheme provides an adaptive edge quality decision mechanism. This mechanism can provide different edge images to meet various applications and the subjective evaluation. Moreover, this scheme locates the edge boundaries to the sub-pixel accuracy, which is an advantage to applications such as data hiding and image watermarking.     

Quadruple Difference Expansion-Based Reversible Data Hiding Method for Digital Images

ABSTRACT

Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but also the ability of cover media to be reconstructed without any distortion. A latest lossless data hiding technique is proposed by Hong Lin Jin's that is based on hiding only one data bit in the spatial domain in gray-level image. However, this method uses double difference expansion to embed bits which results in a small embedding capacity. For this purpose, we propose an improved algorithm with the potential of increasing the payload capacity and maintaining good image quality. The proposed improved algorithm is characterized by two aspects. First, the proposed improved reversible data hiding scheme is enhanced to exhibit data hiding in color palette images. Second, the embedding level is improved by using quadruple difference expansion to guarantee the embedding of 2-bit data into color images. Experiments of the proposed improved method have been conducted over several well-known test images. The results show that the proposed improved method significantly improves the embedding capacity over Hong Lin Jin's scheme by the range of 15–35% for grayscale images and 20–46% for color images while still maintaining the quality of the stego-images.     

利用改进整数变换实现图像无损信息隐藏

为了在图像中实现无损信息隐藏,并提高数据嵌入容量和隐蔽性,给出一种基于改进整数变换的无损信息隐藏方法。首先对图像块采用改进算法进行整数变换,使得变换前后图像块引入失真较少,然后自适应选择整数变换后引入失真较小的可修改图像块进行整数变换,并嵌入较大数据量的机密信息,在公共信道进行传输后接收端能正确提取机密信息,并无损恢复原宿主图像。实验结果表明,该方法具有较大的数据嵌入容量和较好的隐蔽性,可用于医学图像中隐藏病历信息及遥感、军事图像的无损隐秘传输等领域。     

Low-complexity two-step lossless depth coding using coarse Lossy coding

Texture and depth images are generally used for 3D viewing with advanced displays. Because sthe characteristics of a depth image are very different from those of a texture image, an efficient compression method is required to transmit a depth image in a limited bandwidth. In this paper, a low-complexity two-step lossless depth coding (LTLDC) method using coarse lossy coding is proposed. The proposed method downsamples an original image and then coarsely compresses the downsampled image in the first step. This compressed image is upsampled, and then its residual is generated by subtracting the upsampled image from the original image. In the second step, each coding block within the residual and original images is adaptively compressed with a fast mode decision method in a lossless way, and the proposed method determines the best block based on their coding performance. Experimental results show that the proposed LTLDC method achieves a bitrate reduction of 4.35% with encoding complexity reduction of 20.38%.

     

一种基于块中值整数变换的可逆数据隐藏

在基于可逆整数变换的数据隐藏算法中,传统的方法利用左上角像素值与块中其他像素值得到差值。提出的方法利用块中值与块中其他像素值得到差值,差值变小,从而使得藏密图像失真降低。同时该方法采用在平滑区域优先嵌入数据的策略,使藏密图像的信噪比显著提高。将该方法与类似的方法进行实验比较,结果表明提出方法在嵌入相同数据量时确实具有更好的隐蔽性,并且隐密数据和原宿主图像均能从隐藏图像无损恢复,验证了提出算法的有效性。     

密文域高嵌入率图像全位面可逆数据隐藏

目的 针对现有的加密域可逆信息隐藏算法未能充分利用图像的全部位平面的问题,提出了一种密文域高嵌入率图像全位面可逆数据隐藏。方法 对载体图像进行加密,然后将隐蔽信息嵌入到加密图像中,进行隐蔽传输,发送给接收者。本文将灰度图像的8个位平面都用来进行数据嵌入,并把每个位平面划分成不重叠的块,分为非连续块（块内像素值0,1都存在）和连续块（块内为全0或全1像素值）,按块进行重排列且将排列前的块标签嵌入到重排列图像中,使用流密码对图像进行加密。在数据嵌入阶段,提出了带修正信息的像素预测方法用于非连续块的嵌入。连续块中,保持块内右下角像素值不变,用于连续块的恢复,其他位置嵌入数据;非连续块中,对预测正确的像素嵌入数据,预测错误的像素保持不变。结果 实验过程实现了多种密文域可逆数据隐藏算法,本文进行大量对比实验,并在BOSSbase和BOWS-2数据集上进行验证,与其他方法比较,本文方法在BOSSbase和BOWS-2数据集上的嵌入率分别提升了42.1%和43.3%。结论 提出的加密图像可逆数据隐藏方案,通过对不同性质的块采用不同方法进行数据嵌入,利用图像全位面信息,使得方案能够获得更高的嵌入率,表明了本文方法的有效性。     

Adjustable prediction-based reversible data hiding

Reversible data hiding has received considerable interest recently in the field of information hiding. However, most of difference expansion (DE-based) schemes suffer from the problem that image cannot be restored into its original one free from location map. Reversible data hiding scheme with location map cannot effectively raise hiding capacity; in addition, it also incurs the computational cost during embedding and extracting. This study presents a reversible data hiding scheme that is free of location map and thus the computational cost, associated with embedding and extracting, is lower than that of most DE-based schemes. Furthermore, our scheme solves the problem of the low embedding rate of Tseng and Hsieh?s scheme because our scheme takes advantage of the bulk of relatively high small prediction-errors to raise embedding capacity. Experimental results demonstrate that the proposed scheme achieves the larger payload and the better image quality than some existing schemes by an adjustable control factor. The performance of the proposed scheme is more stable for the different images.     

基于自适应MSB与差值预测的大容量密文域可逆信息隐藏算法

为提高密文域可逆信息隐藏（reversible data hiding in encrypted images,RDH-EI）的嵌入容量,提出了一种基于自适应MSB（most significant bit）与差值预测的RDH-EI方案。首先将图像进行分块,然后进行块级加密和置乱以抵抗对于加密图像的分析。在嵌入数据阶段,对于自适应MSB预测的方法进行改进,将没有嵌入数据的块,利用部分块内像素之间差值很小的特点采用自适应差值预测的方法嵌入数据,以块中左上角像素为目标像素,用于预测其他像素从而腾出更多的嵌入空间。实验结果表明,所提方法具有可逆性和可分离性,并且在自适应MSB预测方法的基础上进一步提高了嵌入容量,对于512×512大小的灰度图像,平均嵌入容量提高了大约7 445 bit。     

基于图像块像素差的大容量无损信息隐藏算法

为使提取嵌入的秘密信息后能无损恢复原始载体图像,提出一种基于图像块相邻像素差的大容量无损信息隐藏算法。将原始载体图像进行分块,并按一定顺序扫描图像块得到一系列像素序列,通过计算每个序列中相邻像素的差值得到差值直方图,从中选择2个最大峰值点将秘密信息嵌入。实验结果表明,与其他无损嵌入算法相比,该算法在保证较好不可见性的情况下,信息嵌入容量较大,峰值信噪比较高。     

利用像素置换的自适应可逆信息隐藏

目的 像素置换作为一种可逆信息隐藏方式具有良好的抗灰度直方图隐写分析能力,但嵌入容量偏小一直是其缺陷。针对这一问题,提出了一种基于像素置换的自适应可逆信息隐藏算法。方法 首先,与传统2×2像素块结构相比构造了尺寸更小的像素对结构,使得载体图像可以被更稠密地分割,为嵌入容量的提升提供了基数条件。其次,提出适用于该新像素结构的可嵌像素对（EPP）筛选条件,避免嵌入过程引起图像质量大幅下降。之后,根据EPP的灰度趋势差异对其进行自适应预编码,提高Huffman编码压缩比,进一步提升算法嵌入容量。最终,通过像素置换嵌入信息。结果 与2×2像素块结构的非自适应图像隐写算法相比,在同样保证灰度直方图稳定性的情况下该算法的PSNR提高了32%左右,嵌入容量提高了95%以上。其中自适应性对嵌入容量提升的贡献极大。结论 本文算法同时具有抗灰度直方图隐写分析能力与高嵌入容量性的可逆信息隐藏。算法构造了更高效的可嵌单位,并且针对不同载体图像的特点对其可嵌区域进行差异化编码。实验结果表明,本文算法在具有更好的不可见性的同时,嵌入容量得到大幅提升。     

基于整数小波最优直方图对的图像无损数据隐藏

嵌入位置图法是目前国内外普遍应用的图像数据隐藏方法,但其嵌入量不够大,图像的视觉效果也一般。针对此不足,提出一种基于多灰度图像真实性认证的直方图对无损数据隐藏新方法。该方法对灰度图像进行整数小波变换(IWT),并在直方图上选取最优阈值,通过构造直方图对进行数据嵌入。实验表明,在嵌入容量为0.1bpp时,峰值信噪比达到46dB,嵌入效果较好。     

Separable reversible data hiding in encrypted images based on flexible preservation of the differences

To better protect the security of users’ private data in the cloud environment, the technology for separable reversible data hiding in encrypted images has been attracting increasing attention from researchers. In this paper, we propose a separable reversible data hiding scheme in encrypted images based on the flexible preservation of differences. This scheme has three parts: 1) For the content owner, the original image is divided into non-overlapping blocks, for which block-mean is computed. Then the differences between the values of every pixel and the block-mean are obtained and an initial label map is generated. Because most of the differences tend to concentrate around 0, we use two bits to dynamically record the range of the differences to vacate space for hiding. Further, introducing the block-mean differences also serves to vacate more space, for which the label map is amended accordingly. Finally, the image with free space is encrypted into the encrypted image using an encryption key. 2) For the data hider, the secret bits are embedded into the encrypted image by directly replacing the spare bits without obtaining any information of the original image. 3) For the receiver, he/she can achieve the desired information according to the key in his/her possession. Experimental results show that our proposed scheme is able to achieve an average embedding capacity as large as 1.785 bpp and 1.709 bpp when block size is set to 2?×?2 and 2?×?4, respectively. Comparison with those of previous schemes, the proposed scheme has excellent embedding capacity, especially for smoother images.

     

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.     

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。     

Double linear regression prediction based reversible data hiding in encrypted images

Existing prediction-based works on reversible data hiding in encrypted images usually embed the secret messages by referring to the difference between current pixel and its predicted value. An accurate prediction model may promote an improvement of embedding capacity. Existing schemes, however, may not work well due to involving a bad prediction model so that their embedding capacity cannot be improved further. To address the problem, this paper proposes a new reversible data hiding scheme in encrypted images by designing double linear regression prediction model. Proposed model can significantly improve the prediction accuracy of current pixel based on neighboring pixels, more auxiliary rooms are thus vacated to embed secret data. Furthermore, a prediction error map is constructed to mark the error positions caused by inaccurate prediction, which can be further compressed lossless to lower the capacity of auxiliary data. Reversible recovery for original image can be finally achieved successfully. Experimental results demonstrate that the proposed scheme significantly improves prediction accuracy and data embedding capacity by combining double linear regression prediction model and prediction error map, and then can achieve separable and lossless recovery for the original image. Compared with existing works, the proposed scheme can implement a higher visual quality of decrypted images, while maintaining a larger embedding capacity.