CN114820380B - Spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance - Google Patents
Spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance Download PDFInfo
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Abstract
The invention discloses a method for enhancing an airspace steganographic carrier image based on content self-adaption disturbance resistance, which comprises the following steps of: calculating the multi-granularity image texture characteristics of the carrier image; clustering the carrier images according to pixel values, and segmenting the images according to semantic category labels; calculating a weighted mask of the carrier image; calculating content adaptive countermeasure disturbance; and adding the content adaptive anti-disturbance into the carrier image to obtain an enhanced carrier image, and adding the real secret information to perform steganography to obtain a final enhanced secret-carrying image. The invention adds disturbance noise to the image texture rich area according to the characteristics of the carrier image, and improves the concealment of the disturbance noise. Therefore, the method and the device can successfully attack the deep learning-based steganalysis network under the condition of a small amount of disturbance noise, and can reduce the risk of detecting the anti-disturbance noise.
Description
Technical Field
The invention relates to the technical field of multimedia security, in particular to a spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance.
Background
With the rapid development of communication technology and network information technology, digital images have become a common information exchange mode in people's daily life. The digital image has high redundancy, and if some pixel points of the image are modified, the ornamental value of people on the image cannot be influenced, so that the image has natural advantages in carrying secret information for communication. Image steganography is an important covert communication mode, and secret information is mainly embedded into a common digital image (called a carrier image), and the image embedded with the secret information (called the carrier image) is transmitted in a public channel without causing the doubt of a third party supervisor (such as a steganalyser). The image steganography and steganography analysis technologies are in a game state all the time and develop in opposition to each other. With the rise of deep learning in computer vision, the steganalysis technology based on deep learning is rapidly developed, which brings great challenges to the image steganalysis technology. At present, in order to further resist a steganalysis method based on deep learning, a steganographic party provides two main flow modes: the image steganography method based on the generation of the countermeasure network and the image steganography method based on the countermeasure sample.
Among the patents currently published, there are the following patents that address similar problems with the process of the present invention: the publication number is CN108346125B, which is a patent of airspace image steganography method and system based on generation of countermeasure network. The method mainly comprises the steps of generating a modified probability map from a carrier image through a U-Net network, simulating the modified probability map by combining a hyperbolic sine function to generate a secret-carrying image, simultaneously, mutually confronting with a steganalysis network, obtaining a steganography probability generation model through training, and finally, combining STC (syndrome time coding) to obtain the secret-carrying image. The method effectively improves the security performance of image steganography, but still has the following defects: 1) Because the network structure is huge, the model is still unstable in the training process and is difficult to converge; 2) The generative model is data driven and therefore relies on a training data set, with the security of steganography of the image still being low for different data sets.
Reference [1] (Yiwei Zhang, weiming Zhang, kejiang Chen, jianying Liu, yujia Liu, and Nenghai Yu.2018. Adaptive examplesagainst default near network based on disturbance noise [ C ]// Proceedings of the 6th ACM works hop on Information Hiding and Multimedia security.2018.) discloses a method of adding disturbance noise in a carrier image, which generates disturbance noise using gradient Information of a steganalysis network to an input image, and iteratively adds to the carrier image, generates a disturbance carrier image, then adds secret Information, generates a secret image, and inputs the generated disturbance carrier image to the steganalysis network until the target steganalysis network cannot judge. The method effectively improves the security performance of the steganography, has higher steganography efficiency, and still has the following defects: 1) Because disturbance noise is added in a plurality of iterations until the steganalysis is successfully fooled, the disturbance noise added in the method is large in quantity; 2) The disturbance noise adding mode does not consider image characteristic information and carries out global addition on the image; the defects of the two points show that the scheme is easy to leave relatively obvious disturbance traces in the final generated dense image (especially in the flat area of the image texture).
Reference [2] (Qin C, zhang W, dong X, et al, applied systematic gradient based on sparse noise enhancement [ J ]. Journal of Visual Communication and Image reproduction, 2021, 80.). Discloses a method of adding sparse disturbance noise based on a gradient map to a carrier Image, which visualizes gradient information of an input Image using a steganalysis network, selects a part of values with large Image gradients to iteratively add disturbance noise, generates a confrontation carrier Image, then performs steganography to obtain a carrier Image, and finally inputs the carrier Image into a steganalysis network for judgment until the steganalysis network is successfully fooled. The method can further reduce the redundant points of disturbance noise while maintaining steganography security, but the method still has the defects that: 1) According to the method, in the process of gradient information visualization, the part of the point with the larger gradient value is still in the image flat area, and obvious disturbance traces are still easily left after disturbance noise is added in the part of the area; 2) The method adopts a steganalysis network to calculate the gradient value of an input image, but the gradient value still has no clear physical meaning at present, and the size selection is difficult to define.
Disclosure of Invention
The invention aims to provide a spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance.
The technical scheme for realizing the purpose of the invention is as follows:
a spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance comprises
Step 1: calculating the multi-granularity image texture characteristics of the carrier image c;
1.1 calculating the fine grain texture description value L of each pixel point of the carrier image c fine (-) to obtain a fine-grained texture description feature matrix L fine (ii) a Wherein, the fine grain texture description value L of the pixel (io, jo) fine (. Phi.) is
i o And j o Respectively represent pixel points (i) o ,j o ) The lateral and longitudinal coordinates of; r denotes a pixel point (i) o ,j o ) Randomly selecting one pixel point as a starting point, and numbering according to a clockwise or anticlockwise direction; x is the number of o And x r Respectively representing pixel points (i) o ,j o ) And the pixel values of the neighborhood pixels; s (-) represents a symbolic function of,
1.2 calculating the coarse grain texture description value L of each pixel point of the carrier image c coarse (. To) to obtain a coarse-grained texture description feature matrix L coarse (ii) a Wherein, the coarse grain texture description value L of the pixel (io, jo) coarse (. Phi.) is
The count is 0/1 or 1/0 jump times of a bit string obtained after the value of the 8 neighborhood pixel point is taken through a sign function s (·), and thre is a threshold value;
1.3 calculating the Multi-granularity image texture feature matrix of the Carrier image c
L＝L fine +L coarse ；
And 2, step: carrying out k-means clustering on the carrier image c according to the pixel values to obtain a semantic category label of each pixel value; segmenting according to the semantic category labels to obtain a classified semantic category label M matrix;
and 3, step 3: calculating a weighted mask of the carrier image c;
3.1 calculating the texture of each image block; the image blocks are pixel points with the same semantic category label value M (i, j)
(ii) a set of (i, j);
wherein the texture of the d-th image block
R d Set of pixel values, N, representing the d-th image block d Number of pixel values, L, representing the d-th image block i,j Expressing the texture value of the pixel point (i, j) of the carrier image c, namely the ith row and the jth row in the multi-granularity image texture feature matrix L of the carrier image c
The value of the column;
3.2 computing the weighted mask matrix of the Carrier image c
Wherein, the first and the second end of the pipe are connected with each other,
The value of the ith row and the jth column; th is a threshold value of texture;
and 4, step 4: calculating content adaptive countermeasure disturbance;
4.1 steganography is carried out on the carrier image c to obtain a secret carrier image s; inputting carrier image c and carrier image s into steganalysis network
Training to obtain a steganalysis network model;
4.2 initializing the countermeasure disturbance δ adv Will oppose the disturbance delta adv Adding the obtained mixture into a carrier image c to obtain an enhanced carrier image c adv (ii) a Adding random secret information pair c adv Steganography is carried out to obtain the current enhanced secret-carrying image s adv ；
4.3 in the process of backward propagation of the steganalysis network model, solving the current enhanced secret-carrying image s adv Of the gradient value, updating the counterdisturbance, i.e.
Wherein, | | δ adv || 2 Is the 2-norm of the current countermeasure disturbance, k is the weighting coefficient,
is a steganographic analysis network>
Cross entropy loss function of (1);
4.4 Using weighted mask matrix
Weighting the updated counterdisturbance to obtain weighted counterdisturbance, wherein the weighted counterdisturbance is used as the current counterdisturbance:
4.5 adding the current counterdisturbance δ to the Carrier image c adv Obtaining an enhanced carrier image c adv Adding a random secret information pair c adv Steganography is carried out to obtain the current enhanced secret-carrying image s adv (ii) a Using steganalysis networks
Determining a current enhanced secret-carrying image s adv : if the steganalysis network->
The current enhanced secret-carrying image s adv If the carrier image c is determined, the current counterdisturbance delta is made adv Adaptive disturbance resistance is carried out on the content, otherwise, the step 4.3 is returned;
and 5: adapting content against disturbance delta adv Adding the obtained mixture into a carrier image c to obtain an enhanced carrier image c adv Adding true secret information pair c adv Steganography is carried out to obtain a final enhanced secret-carrying image s adv 。
Preferably, the steganography adopts HUGO, WOW, UNIWARD or HILL algorithm; the steganalysis network
XuNet, yeNet, SRNet or ZhuNet.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention adds disturbance noise to the image texture rich area according to the characteristics of the carrier image, and improves the concealment of the disturbance noise. Therefore, the method and the device can successfully attack the deep learning-based steganalysis network under the condition of a small amount of disturbance noise, and can reduce the risk of detecting the anti-disturbance noise.
2. The invention constructs a weighted disturbance resisting mask mode. Firstly, an image texture description method based on multi-granularity is designed, and a pixel-by-pixel fine granularity and image feature statistics coarse granularity mode is combined for describing image texture features, so that compared with the mode of singly adopting the pixel-by-pixel fine granularity to describe image textures, the method is more robust to tiny noise. Secondly, the image is segmented according to the semantic features of the image, an image texture information rich block is selected as a candidate disturbance position, and a disturbance mask is constructed, so that the anti-disturbance noise has the image content self-adaption performance. Therefore, the method is more suitable for application scenes for accurately performing image steganography.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2a, 2b and 2c are schematic diagrams of enhanced carrier image visualization of the present invention.
Detailed Description
The invention is further illustrated by the following specific examples.
As shown in fig. 1, the method for enhancing an image of a spatial domain steganographic carrier based on content adaptive disturbance rejection includes:
step 1: calculating the multi-granularity image texture characteristics of the input carrier image c, wherein the method comprises the following steps:
step 1.1: and comparing pixel values of 8 neighborhood pixel points pixel by pixel aiming at the carrier image, wherein if the central pixel value is larger than the neighborhood pixels, the pixel value is 1, and otherwise, the pixel value is 0. In this embodiment, the upper left corner of the neighborhood is set as the starting point, 8 values 0/1 are recorded counterclockwise as a bit string with a length of 8, and the bit string is converted into a decimal number to represent the texture value of the current pixel, so that the fine-grained texture description value L of each pixel is obtained fine (. Cndot.) is:
wherein i o And j o Respectively representing the central pixel coordinate value, x o And x r Are respectively provided withExpressing gray values of the central pixel point and the neighborhood pixel point, and s (-) expresses a symbolic function:
step 1.2: based on the value of 0/1 bit of 8 neighborhood of each pixel value obtained in step 1.1, similarly setting the upper left corner of the neighborhood value as a starting point, recording 8 neighborhood values of each pixel counterclockwise as a bit string with the length of 8, and counting the number of 0/1 or 1/0 jump times count of the bit string, if the jump times are smaller than a threshold value thre, the texture complexity is 1, otherwise, the texture complexity is accumulated, the texture complexity is a coarse-grained texture description value, and the coarse-grained texture description value L of each pixel point coarse (. Cndot.) is:
step 1.3: the fine-grained texture description characteristics L of the images respectively obtained in the step 1.1 and the step 1.2 fine And image coarse grain texture description feature L coarse And adding pixel by pixel to obtain the final texture feature L of the image: l = L fine +L coarse Wherein L is fine ，L coarse And L each represents a matrix having the same size as the original image.
Step 2: carrying out k-means clustering on the input carrier image c according to the pixel values to obtain a semantic category label of each pixel value; and (4) segmenting according to the semantic category label to obtain a classified semantic category label M (the size of the matrix M is the same as that of the original image), wherein the set of the pixel points (i, j) with the same semantic label value M (i, j) belongs to the same image block.
And step 3: inputting the texture value matrix L of the carrier image c obtained in the step 1 and the semantic label M after the carrier image c obtained in the step 2 is classified, and calculating an image weighting mask, wherein the image weighting mask comprises the following steps:
step 3.1: carrying out processing on a carrier image c by adopting the texture value matrix obtained in the step 1Assigning a value, calculating a texture mean value (i.e. the texture mean value of each image block) of the same label of the image pixel according to the semantic label M obtained in the step 2, wherein the texture mean value is used for describing the texture of the current segmentation image block, and then the texture L of the d-th image block d Comprises the following steps:
wherein R is d Set of pixel values, N, belonging to the d-th image block d Number of pixel values, L, representing the d-th image block i,j Representing a texture value with (i, j) image pixel coordinates.
Step 3.2: selecting an image block according to a threshold th, and selecting a sub-block with rich texture of the current image as a candidate weighting mask:
wherein the content of the first and second substances,
representing the texture value with the resulting image pixel coordinates (i, j). Then: />
(the matrix size is the same as the original carrier image size) represents the final resulting mask.
And 4, step 4: calculating disturbance noise, comprising:
step 4.1: steganography is carried out on the input carrier image c by adopting a traditional steganography method (such as a height undetectable algorithm HUGO, a wavelet weight algorithm WOW, a airspace unified relative wavelet algorithm UNIWARD, a high-pass low-pass filter algorithm HILL and the like) to obtain a corresponding secret-carrying image s, and the carrier and the secret-carrying image pair are input into a steganography analysis network
(e.g., xuNet) [3] 、YeNet [4] 、SRNet [5] 、ZhuNet [6] Equal steganalysis network) to obtain a steganalysis network model;
reference [3]: xu, H. -Z.Wu, and Y. -Q.Shi, "Structural design of connected neural networks for hierarchical," IEEE Signal Process.Lett., may 2016, vol.23, no.5, pp.708-712.
Reference [4]: J.Ni, J.Ye, and Y.I.Yang, "Deep learning resonant representation for image segmentation," IEEE Trans. Inf. Forces Security, nov.2017, vol.12, no.11, pp.2545-2557.
Reference [5]: boroum and, M.Chen, J.Fridrich, deep residual network for stepanalysis of digital images, IEEE Trans. Inf. Forensecs Secur.14 (5) (2019) 1181-1193.
Reference [6]: zhang R, zhu F, liu J, et al, depth-wise minor adjustments and multi-level posing for an effective specific CNN-based catalysis [ J ]. IEEE Transactions on Information forms and Security,2019, 15.
Step 4.2: initializing the countering disturbance noise delta adv Will oppose the disturbance noise delta adv Adding to the carrier image c to obtain an enhanced carrier image c adv And adding a pair of random secret information c adv Performing steganography to obtain a current enhanced secret-carrying image s adv ；
Step 4.3: computing 2-norm [ Delta ] of the anti-disturbance noise adv || 2 As a constraint to minimize the countering disturbance noise. Meanwhile, calculating a cross entropy loss function L of the steganalysis network D (f c,s (theta; t)). To the current enhanced secret-carrying image s in the steganalysis network model back propagation process adv Obtaining a gradient value:
where k is a weighting coefficient, L D (. Is a loss function of the steganalysis network;
step 4.4: for the disturbance value delta calculated in the step 4.3 adv Weighting the mask obtained in step 3
Obtaining a current disturbance value: />
Step 4.5: adding the current countering disturbance noise delta to the carrier image c adv Obtaining the challenge vector c adv Then adding random secret information to steganographically obtain enhanced secret-carrying image s adv And will enhance the secret-carrying image s adv Input to an analyzer
Is determined if the steganalyser->
If the secret image s is judged as a carrier image, the anti-disturbance noise delta is output adv As content self-adaptive anti-disturbance noise, otherwise, continuously iterating the steps 4.3-4.5 to add the anti-disturbance noise;
and 5: adaptive disturbance noise resisting delta of content obtained in step 4 adv Adding the obtained mixture into a carrier image c to obtain an enhanced carrier image c adv And embedding real secret information, and performing steganography according to the traditional image steganography algorithm to obtain a final enhanced secret-carrying image s adv 。
Taking zhuet as an example, the invention and its safety performance ratio are as follows:
table 1. False alarm rate p of different carrier enhancement methods for detecting based on deep learning steganalyser ZhuNet MD (％)
In the table: the load (bpp, bit per pixel) refers to the secret information carrying capacity of the original steganography method, namely the bit number of each pixel embedded with secret information in the carrier image on average; the modification rate is the ratio of the number of modifications of the confrontation sample to the carrier image.
False alarm rate p MD The definition is as follows:
wherein N is r Representing the number of secret-carrying images judged by the steganalysis network as carrier images, N t Indicating the number of all the secret images.
As shown in Table 1, the method of the invention can make the hidden writing analyzer ZhuNet network based on deep learning have the highest false alarm rate and the best attack effect. For example, under the conditions that the load is 0.1bpp and the modification rate is 0.5, the method provided by the invention compares the SPS-ENH false alarm rate p which is the best method at present MD The improvement is 19.3 percent. In addition, with the improvement of the modification rate, the false alarm of all the methods is improved, which shows that when the anti-disturbance intensity is larger and larger, the successful attack can be basically realized on the steganalysis device ZhuNet network.
As shown in fig. 2a, 2b and 2c, fig. 2a is the original carrier image, the picture being selected from the BOSSBase data set. Fig. 2b shows the added noise immunity of the method of the present invention, the white point is the modified pixel area, the black point is the unmodified pixel area, and the modification amplitude is 1, so the graph shows the result after the pixel value is amplified by x255 times. Fig. 2c is the enhanced carrier image effect after adding the anti-disturbing noise to the carrier image of fig. 2 a. The white dashed box region represents an example of a region where the image texture is richer. As can be seen from FIG. 2, the disturbance information added by the present invention is mostly located in the image texture rich region.
Claims (2)
1. The spatial domain steganographic carrier image enhancement method based on content self-adaption disturbance resistance is characterized by comprising the following steps
Step 1: calculating the multi-granularity image texture characteristics of the carrier image c;
1.1 calculation loadFine-grained texture description value L of each pixel point of volume image c fine (-) to obtain a fine-grained texture description feature matrix L fine (ii) a Wherein, the fine grain texture description value L of the pixel (io, jo) fine (. Phi.) is
i o And j o Respectively represent pixel points (i) o ,j o ) The lateral and longitudinal coordinates of (a); r denotes a pixel point (i) o ,j o ) Randomly selecting one pixel point as a starting point, and numbering according to a clockwise or anticlockwise direction; x is the number of o And x r Respectively represent pixel points (i) o ,j o ) And pixel values of the neighborhood pixels; s (-) represents a symbolic function,
1.2 calculating the coarse grain texture description value L of each pixel point of the carrier image c coarse (. To) to obtain a coarse-grained texture description feature matrix L coarse (ii) a Wherein, the coarse grain texture description value L of the pixel (io, jo) coarse (. Phi.) is
The count is 0/1 or 1/0 jump times of a bit string obtained after the value of 8 neighborhood pixel points is taken through a sign function s (·), and the thre is a threshold value;
1.3 calculating the Multi-granularity image texture feature matrix of the Carrier image c
L＝L fine +L coarse ；
And 2, step: carrying out k-means clustering on the carrier image c according to the pixel values to obtain a semantic category label of each pixel value;
segmenting according to the semantic category labels to obtain a classified semantic category label M matrix;
and step 3: calculating a weighted mask of the carrier image c;
3.1 calculating the texture of each image block; the image blocks are a set of pixel points (i, j) with the same semantic category label value M (i, j);
wherein the texture of the d-th image block
R d Set of pixel values, N, representing the d-th image block d Number of pixel values, L, representing the d-th image block i,j Expressing texture values of pixel points (i, j) of the carrier image c, namely values of an ith row and a jth column in a multi-granularity image texture feature matrix L of the carrier image c;
3.2 computing the weighted mask matrix of the Carrier image c
Wherein the content of the first and second substances,
and 4, step 4: calculating content adaptive countermeasure disturbance;
4.1 steganography is carried out on the carrier image c to obtain a secret carrier image s; inputting carrier image c and carrier image s into steganalysis network
Training to obtain a steganalysis network model;
4.2 initializing the countermeasure disturbance δ adv Will oppose the disturbance delta adv Adding the obtained mixture into a carrier image c to obtain an enhanced carrier image c adv ；
Adding random secret information pair c adv Steganography is carried out to obtain the current enhanced secret-carrying image s adv ；
4.3 in the process of backward propagation of the steganalysis network model, solving the current enhanced secret-carrying image s adv Of the gradient value, updating the counterdisturbance, i.e.
Wherein, | | δ adv || 2 Is the 2-norm of the current countermeasure disturbance, k is the weighting coefficient,
is a steganalysis network
Cross entropy loss function of (1);
4.4 Using weighted mask matrix
Weighting the updated counterdisturbance to obtain weighted counterdisturbance, wherein the weighted counterdisturbance is used as the current counterdisturbance:
4.5 adding the current counterdisturbance δ to the Carrier image c adv Obtaining an enhanced carrier image c adv Adding random secret information pair c adv Steganography is carried out to obtain the current enhanced secret-carrying image s adv (ii) a Using steganalysis networks
Determining a current enhanced secret-carrying image s adv : if it is notSteganalysis network->
The current enhanced secret-carrying image s adv If the carrier image c is determined, the current counterdisturbance delta is made adv Adaptive countermeasure disturbance for the content, otherwise return to step 4.3;
and 5: adapting content against disturbance delta adv Adding the obtained mixture into a carrier image c to obtain an enhanced carrier image c adv Adding true secret information pair c adv Steganography is carried out to obtain a final enhanced secret-carrying image s adv 。
2. The method for enhancing the spatial domain steganographic carrier image based on content adaptive robust to disturbance as claimed in claim 1, wherein the steganography employs HUGO, WOW, UNIWARD or HILL algorithm; the steganalysis network
XuNet, yeNet, SRNet or ZhuNet. />
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