Reversible data hiding for high quality images exploiting interpolation and direction order mechanism

Reversible data hiding can restore the original image from the stego image without any distortion after the embedded secret message is exactly extracted. In this paper, a novel, reversible, data hiding scheme for high quality images is proposed in spatial domain. To increase embedding capacity and enhance image quality, the proposed scheme classifies all pixels as wall pixels and non-wall pixels. For wall pixel, the interpolation error is used to embed secret data over the interpolation prediction method. In contrast, the difference value between the non-wall pixel and its parent pixel, which is defined by the direction order, is computed to hide secret data based on the histogram shifting. The experimental results demonstrate that our scheme provides a larger payload and a better image quality than some existing schemes. Moreover, the performance of the proposed scheme is more stable for different images.     

Reversibility of image with balanced fidelity and capacity upon pixels differencing expansion

Reversible data hiding has attracted considerable attention in recent years. Being reversible, the decoder can extract hidden data and recover the original image completely, and the difference expansion (DE) scheme can lead to a lossless pixel after secret data exacting. Furthermore, despite achieving pixel reversibility based on the concept of expanded differencing, the difference expansion scheme can cause enormous image distortion because of the size of the difference. The proposed scheme in this paper describes a novel prediction for achieving predictive error based reversible data hiding by considering the relation between a pixel and its neighboring pixel and using the predictor to identify the projected difference in pixel value. Experimental results show that the proposed scheme is capable of providing great embedding capacity without causing noticeable distortion by selecting the minimal predictor based on pixel expansion. In multilevel cases, this proposed method performs better than other existing methods. Moreover, the proposed scheme is able to pass the Chi-square test, a test used to find whether an image utilizes LSB for data hiding.     

Prediction-based reversible data hiding

For some applications such as satellite and medical images, reversible data hiding is the best solution to provide copyright protection or authentication. Being reversible, the decoder can extract the hidden data and recover the original image without distortion. In this paper, a reversible data hiding scheme based on prediction error expansion is proposed. The predictive value is computed by using various predictors. The secret data is embedded in the cover image by exploiting the expansion of the difference between a pixel and its predictive value. Experimental results show that our method is capable of providing a great embedding capacity without making noticeable distortion. In addition, the proposed scheme is also applicable to various predictors.     

基于非对称直方图平移的可逆信息隐藏算法

利用两个非对称直方图分别向相反方向平移嵌入信息,会产生像素值的补偿还原效应,提出了一种更好的像素值预测方法,生成两个更偏向0值右侧和左侧的非对称预测误差直方图,这样的两个直方图在进行第二层信息嵌入时,会出现更多的像素点恢复到原始图像像素值,减少图像扭曲失真,提高载密图像质量。与传统算法相比,减少了参与直方图修改的像素量,进一步保护了载密图像质量。     

High capacity reversible hiding scheme based on interpolation,difference expansion,and histogram shifting

Difference expansion and histogram shifting methods are two popular hiding strategies that have been widely used in many researches. For example, Hong and Chen developed a reversible hiding method based on interpolation and histogram shifting. The image quality of their scheme is exceptional; however, their scheme needs to keep and transmit two peak points for secret data extraction and pixel recovering. Moreover, the reference pixels in their scheme cannot be used to embed secret data that will decrease the hiding capacity. Therefore, this paper shall propose a reversible hiding method to enhance their scheme. The proposed method applies the difference expansion, histogram shifting and interpolation strategies to conceal secret data in the reference pixels for increasing the hiding payload. Experimental results indicate that the proposed method performs better in terms of hiding capacity than recently developed methods.     

Development of a data hiding scheme based on combination theory for lowering the visual noise in binary images

In order to raise the embedding capacity and simultaneously reduce the artifact effect caused by embedding secret messages into binary images, a novel data hiding method based on the combination theory is proposed. In the proposed scheme, a secret position matrix is designed to improve the hiding capacity which is capable of preventing the least distortion based on the combination theory. Our new scheme enables users to conceal more than one bit of secret data by changing at most one pixel in one subimage. We have derived a formula for computing the payload and the possible modification pixels of a block. Compared with the existing schemes in terms of the hiding capacity and the visual artifacts, as our experimental results show, the proposed scheme is capable of providing a better image quality protector even with a more efficient secret data hider.     

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.     

Multiple predictors hiding scheme using asymmetric histograms

In recent years, many data hiding techniques have been proposed, and they can be generally classified into two types according to the reversibility of the image; these two types are reversible and irreversible data hiding. This study focused on reversible data hiding, which makes recovering the cover image possible after the secret data has been extracted. In 2013, Chen et al. proposed an asymmetric-histogram reversible data hiding method. In their scheme, two prediction error histograms (maximum and minimum error histograms) were used to embed the secret message. Two histograms were shifted in opposite directions. Hence, some stego-pixels were shifted to their original values. The complementary embedding strategy is effective. However, the predictor in the method is rough. Only neighboring pixels were used to generate the prediction errors, thereby resulting in poor prediction efficiency. To enhance the prediction efficiency, this paper combines several well-known predictors such as gradient adjusted gap (GAP), median edge detect, and interpolation by neighboring pixel (INP) to generate prediction errors. Different predictors along with the asymmetric-histogram method can achieve better results. The predictor GAP used more neighboring pixels to obtain the prediction value; therefore, it is suitable for complex images. However, the predictor INP only considers that closer pixels can achieve great results for smooth images. Hence, the proposed scheme combines GAP and asymmetric histogram for complex images. However, the predictor INP along with asymmetric histogram is used for smooth images. Experimental results showed that the PSNR value of the proposed method is greater than that of the asymmetric-histogram shifting method and other recent approaches.     

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。     

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.

     

增强图像平滑度的可逆信息隐藏方案

随着互联网技术的发展和社交网络的普及,可逆信息隐藏技术因其具有无损恢复载体信息的特性而被广泛应用于医疗、军事等领域的隐蔽信息传输。传统的可逆信息隐藏方案大多聚焦于嵌入容量提升和载密图像失真率降低,并未过多关注人们对图像视觉细节的要求,难以抵抗隐藏信息检测方法。针对上述挑战,从增强图像视觉平滑度方面入手,提出了一种增强图像平滑度的可逆信息隐藏方案,在嵌入隐蔽信息的同时提升载密图像最终的视觉质量。具体来说,所提方案将目标图像分为参考区域与非参考区域,利用非参考区域的图像像素预测值与原始像素值的差值作为信息嵌入的判断依据,通过差值平移来嵌入信息;进而构造图像平滑机制,采用高斯滤波作为秘密信息嵌入时像素值修改的模板,对预测值进行滤波计算,将滤波差值无损地加入载体图像中,以达到图像平滑的效果;同时将参考区域的像素值作为边信息,用于实现信息提取方对原始载体图像和秘密信息的无损恢复和提取;并以高斯函数中的滤波系数作为预置秘密信息对嵌入信息进行加密处理以保证嵌入信息的机密性。大量经典图像数据集的测试与分析结果表明,所提方案处理过的载密图像视觉平滑度得到了显著增强,具有较低的失真率、较高的嵌入率和较高的嵌入提取效率。在典型环境下,其生成的载密图像与高斯滤波后的图像相似度可达0.996 3,且可获得37.346的峰值信噪比和0.328 9的嵌入容量。     

一种基于像素预测的图像可逆信息隐藏策略

基于像素点预测的可逆信息隐藏(Reversible Data Hiding,RDH)是当今一种低失真、高容量的信息隐藏策略,特别是对于差值扩展和直方图平移算法而言,准确预测可以同时提高数据容量和减小图像失真。文中提出了一种基于像素预测差的直方图平移方案。首先,采用Warped Distance算法来对像素值进行预测,并在此基础上利用图像的局部梯度来实现对像素值的更精确预测。综合上述两种策略,改进了现有的直方图平移算法,同时也给出了防止像素值在平移后溢出的建议。实验结果表明,相对于近年来的其他方案,该方案能够在保证图像质量的情况下有效提升数据嵌入量,并且通过调整数据嵌入层级,可以在具体使用该算法时针对图像质量和数据嵌入量进行权衡。这也进一步说明了利用图像的局部特性,特别是梯度与图像局部几何相似性,可以有效提升像素预测的精度,从而改善可逆信息隐藏的容量-失真性能。     

Adaptive (<Emphasis Type="Italic">k</Emphasis>, <Emphasis Type="Italic">F</Emphasis><Subscript>1</Subscript>) interpolation-based hiding scheme

In 2012, Lee et al. proposed an interpolation technique with neighboring pixels (INP) as the base to conceal secret information in predicted pixels. Their method can effectively predict the pixel between two neighboring pixels. However, the different lengths of secret messages caused great distortion when a large secret message was concealed in the predicted value. Therefore, the proposed scheme applies the center folding strategy to fold the secret message for reducing image distortion. Furthermore, the proposed scheme references the variance of the neighboring pixel to determine the length of the secret message for controlling image quality. The parameter pair (k, F ₁) is used to categorize the variance and determine the size of the secret message hidden in each category. k is the total number of thresholds which computed based on the characteristics of each image for balancing hiding payload and image quality. F ₁ is the length of the secret message for the smoothest area. The experimental results show that the embedding capacity of the proposed method is 1.5 bpp higher than that of existing methods. For the same hiding payload, the image quality of the proposed method is 1.6 dB higher than that of existing methods.     

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。     

Reversible data hiding for high quality images using modification of prediction errors

In this paper, a reversible data hiding scheme based on modification of prediction errors (MPE) is proposed. For the existing histogram-shifting based reversible data hiding techniques, though the distortion caused by embedding is low, the embedding capacity is limited by the frequency of the most frequent pixel. To remedy this problem, the proposed method modifies the histogram of prediction errors to prepare vacant positions for data embedding. The PSNR of the stego image produced by MPE is guaranteed to be above 48 dB, while the embedding capacity is, on average, almost five times higher than that of the well-known Ni et al. techniques with the same PSNR. Besides, MPE not only has the capability to control the capacity-PSNR, where fewer data bits need less error modification, but also can be applied to images with flat histogram. Experimental results indicate that MPE, which innovatively exploits the modification of prediction errors, outperforms the prior works not only in terms of larger payload, but also in terms of stego image quality.     

基于预测误差和直方图对的无损数据隐藏方法

针对目前存在的大部分图像数据隐藏方法的嵌入量不大和视觉效果一般的问题,提出一种基于预测误差和直方图对的无损数据隐藏方法。在多灰度图像中,用某一像素点周围的八个像素点来预测中心像素点的灰度值,同时得到预测误差。再利用直方图对的方法,把数据嵌入到预测误差中。在嵌入过程中,以嵌入阈值和起伏阈值作为指导进行嵌入,通过调整这两个阈值,得到最佳的嵌入效果。实验表明,该方法在视觉效果和嵌入容量方面都取得很好的效果。     

An image interpolation based reversible data hiding scheme using pixel value adjusting feature

In this paper, we propose an image interpolation based reversible data hiding scheme using pixel value adjusting feature. This scheme consists of two phases, namely: image interpolation and data hiding. In order to interpolate the original image, we propose a new image interpolation method which is based on the existing neighbor mean interpolation method. Our interpolation method takes into account all the neighboring pixels like the NMI method. However, it uses different weight-age as per their proximity. Thus, it provides the better quality interpolated image. In case of data hiding phase, secret data is embedded in the interpolated pixels in two passes. In the first pass, it embeds the secret data into the odd valued pixels and then in the second pass, the even valued pixels are used to embed the secret data. To ensure the reversibility of the proposed scheme, the location map is constructed for every pass. Basically, the proposed scheme only increases/decreases the pixel values during data hiding phase, which improves the performance of the proposed scheme in terms of computation complexity. Experimentally, our scheme is superior to the existing scheme in terms of data hiding capacity, image quality and computation complexity.     

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。     

Reversible data hiding based on PDE predictor

In this paper, we propose a prediction-error expansion based reversible data hiding by using a new predictor based on partial differential equation (PDE). For a given pixel, PDE predictor uses the mean of its four nearest neighboring pixels as initial prediction, and then iteratively updates the prediction until the value goes stable. Specifically, for each pixel, by calculating the gradients of four directions, the direction with small magnitude of gradient will be weighted larger in the iteration process, and finally a more accurate prediction can be obtained. Since PDE predictor can better exploit image redundancy, the proposed method introduces less distortion for embedding the same payload. Experimental results show that our method outperforms some state-of-the-art methods.     

A high payload steganographic algorithm based on edge detection

The least-significant-bit (LSB) technique is one of the commonly used steganographic algorithms in the spatial domain. In most existing schemes, they didn’t carefully analyze the relationship between the image content itself. Hence, the smooth areas in the cover image will inevitably be contaminated after hiding even at a low embedding rate, thereby leading to poor visual quality and low security. In recent years, diverse steganography methods using edge detection have been proposed. However, their schemes employ certain pixels in the cover image for the sake of storing edge information, resulting in significant embedding distortion and low payload. In this study, a novel steganography approach based on the combination of LSB substitution mechanism and edge detection is proposed. To avoid the excavation of human visual system (HVS) when more secret bits are embedded into pixels, we classify the cover pixels into edge areas and non-edge areas. Then, pixels that belong to the edge area are used to carry more secret bits. In addition, to further increase the payload as well as preserve good image quality, we adopt a skillful way that the edge information is determined by most significant bits (MSBs) of the cover image so that it does not need to be stored. In the extraction phase, the same edge information is obtained. Therefore, the secret data can be correctly extracted without confusion. The experimental results demonstrate that our scheme achieves a much higher payload and better visual quality than those of state-of-the-art schemes.