CN113436054A - Super-resolution network-based image side information estimation steganography method and storage medium - Google Patents
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Abstract
The invention relates to an image side information estimation steganography method and a storage medium based on a super-resolution network, which realize the following steps through computer equipment, and utilize the super-resolution network to amplify a carrier image to generate an estimation pre-carrier; then, the estimation pre-carrier is sampled with floating point type precision to obtain an unquantized carrier, and the quantization error generated in the downsampling process can be estimated by subtracting the original carrier; adjusting initial symmetric distortion to generate final asymmetric distortion by using the polarity guidance of the estimated quantization error; finally, under the framework of minimizing distortion, the secret message is embedded by using an encoding technology to generate a secret image. The method adopts a super-resolution network to estimate the down-sampling side information to guide the modification of the distortion function, and the strategy of modifying the distortion only considers the polar direction of the quantization error; the steganography scheme of the invention does not need to have an original carrier image, can obtain higher steganography safety and has wider application scenes.
Description
Technical Field
The invention relates to the technical field of steganography, in particular to a super-resolution network-based image side information estimation steganography method and a storage medium.
Background
Firstly, steganography is a covert communication technology, secret information is embedded by slightly modifying elements on a carrier, the carrier embedded with the secret information is sent to a receiving party, the receiving party can finish correct extraction of the secret information, and one of the most key problems in the process is that the secret information cannot be found by a third party, namely steganography safety; the steganographic analyzer can detect whether the carrier is embedded with the message to a certain degree, so the security of steganographic is generally measured by the capability of the steganographic analyzer to resist steganographic detection, that is, the more difficult the carrier and the carrier after the embedded message are distinguished by the steganographic analyzer, the higher the security of steganographic is.
The image steganography is divided into airspace steganography and JPEG domain steganography, and the airspace steganography and the JPEG domain are respectively used as carriers for steganography. The main defects of the existing airspace steganography technology are that the safety is insufficient or the technology is difficult to adapt to the actual application scene, and the behavior of embedding the secret message is easily detected by the existing steganography analyzer in the actual application.
Therefore, the problem to be solved by the invention is to improve the spatial steganography security in the actual scene, namely, the spatial steganography is expected to be carried out by using the method of the invention, so that the existing steganography analyzer is difficult to distinguish whether the carrier image is modified. In fact, when the steganography method is used for resisting detection of various existing steganography analyzers, the safety is obviously improved, and the possibility of detecting steganography behaviors can be effectively reduced.
The following are related technical terms or concepts of the present invention:
carrier image: an image carrying the secret message, the secret message being embedded onto the carrier image in a steganographic process.
Carrying out secret image: the secret image is obtained after embedding the secret message on the carrier image.
Spatial domain images: the space domain is also called an image space and is a space formed by image pixels. Spatial domain image steganography, namely embedding a secret message by adding and subtracting one modification to an image pixel value.
Embedding rate: in the space domain image, the embedding rate is the ratio of the bit length of the embedded information to the number of image pixel points.
Distortion function: by quantifying the influence of each pixel point on the carrier image being modified, modification distortion is assigned to each pixel point, which guides the preference of the pixel modification position in the embedding process. In additive steganography conditions, the sum of the effects of all modifications represents the overall distortion between the carrier image and the secret image. The additive steganography refers to that the influence generated by modification of each pixel point is considered independently, and if the influence generated by joint modification between adjacent pixel points is considered, the additive steganography is called as non-additive steganography. Many existing distortion function algorithms for spatial domain images are based on additive steganography conditions, such as HUGO, WOW, S-UNIWARD, HILL, MVGG. These distortion functions give equal distortion to the addition, subtraction and modification when a pixel is embedded in a secret message, which we call symmetric distortion functions. On the contrary, the asymmetric distortion function gives the pixel points plus or minus a distortion with unequal modification.
STC: and checking the sub-lattice coding. The minimized distortion steganography framework is a mainstream framework for realizing safe steganography, and the STC coding can be close to the theoretical lower bound of the overall distortion under the given embedding rate aiming at any additive distortion function.
Pre-carrier: the carrier image is typically generated from an original image, referred to as a pre-carrier, by an image processing operation.
Side information: and guiding the adjustment of the initial symmetric distortion function as auxiliary information to obtain an asymmetric distortion function which is more favorable for steganography safety. The side information can be usually obtained by the pre-carrier in the process of generating the carrier image by image processing, such as quantization error generated by processes of sampling, image compression, color transformation, etc.
Steganalysis device: steganographic security is detected by distinguishing a carrier image from a secret image. Existing common steganalysis devices are classified into two categories: the method comprises the following steps of firstly, extracting a steganalyser such as an SRM (sequence-related language) based on manual features; the other is a network steganalyser based on deep learning, such as SRNet, YeNet and the like.
Super-resolution: the task is to restore a High Resolution (HR) image according to a Low Resolution (LR) image, and with the development of deep learning, various super-Resolution methods based on a deep learning network are available, such as SRResNet, SRGAN, EDSR, RRDBNet, ESRGAN, RCAN, and the like.
Just as it is now possible to implement an optimal embedding process using STC or the like, given an embedding rate and a distortion function, a major research direction in the field of image steganography today is how to design a better distortion function. Because the overall impact of modifying pixel values in areas of different image texture complexity is different, the traditional additive symmetric distortion function gives lower modification distortion in the areas of complex image texture and higher modification distortion in the areas of smooth texture.
Under the framework of minimizing distortion steganography, the additive symmetric distortion function can make modification points concentrated in a texture complex area as much as possible, so that a steganography analyzer is difficult to distinguish a carrier image from a secret image. But due to the natural nature of the image, the modified distortion of the pixel values plus minus one should not always be equal, so an additive asymmetric distortion function occurs. The additive asymmetric distortion function can be obtained by adjustment on the basis of the additive symmetric distortion function, so that the information for guiding the adjustment process and the adjustment strategy become the key for designing the additive asymmetric distortion function.
Illustrating the technical problems of the prior art solutions:
method for steganography of original down-sampling edge information of spatial domain image
In a real scene, a carrier used by a steganographer for embedding a message is mostly subjected to various image processing operations, such as down-sampling, color conversion, lossy compression, and the like, and side information generated in the image processing process can be utilized by the steganographer to improve steganography security. The method for steganography of the original downsampling edge information of the spatial domain image is characterized in that quantization errors generated in the downsampling process by utilizing an original image corresponding to a carrier, namely a pre-carrier, are used for adjusting initial distortion of carrier elements +/-1 modification, so that the steganography safety is obviously improved. The flow chart of the scheme is shown in figure 1 and is described in detail as follows:
definition u ═ (u)1,u2,…,un) For pixel values that are not quantized and rounded after downsampling of the HR image, x ═ x (x)1,x2,…,xn) The rounded vector pixel value for quantization, i.e., x ═ round (u); y ═ y1,y2,…,yn) Are secret-carrying image pixel values. According to the carrier pixel point xiQuantization error e ofi＝ui-xi，(|eiI is less than or equal to 0.5, i is less than or equal to 1 and less than or equal to n) adjusting xiInitial distortion of + -1
In the formula (I), the compound is shown in the specification,
for a set of various symmetric distortion functions,
can be calculated from any symmetric distortion function,
it represents an asymmetric distortion of the original side-information steganography method. By using
Is shown in
The method is an original side information steganography method of an initial distortion function. In the distortion adjustment process, if the modified pixel value y is obtainediCloser to the pixel value u before roundingiThis modification is given less distortion, leading the steganographically rendered image closer to the original state of the image.
For optimal embedding simulator in embedding secret message
Etc. 2010), corresponding modification probabilities may be calculated from the modification distortions adjusted by the side information
Can be prepared from
And λ is calculated by equation (2), where the value of λ is determined by equation (3).
In equation (3), m is the bit length of the embedded message.
Compared with the traditional steganography based on the symmetric distortion function, the scheme has the advantage that the safety can be remarkably improved. However, as can be seen from the flowchart, the steganographer needs to have the pre-carrier image to execute the scheme, and in most real scenes, the steganographer is often only used for the embedded carrier image, but the corresponding pre-carrier is difficult to obtain, and cannot extract the side information in the image processing process, so that the method is difficult to be widely applied to the real scenes.
Disclosure of Invention
The invention provides an image side information estimation steganography method based on a super-resolution network and a storage medium, which can solve the technical problem.
In order to achieve the purpose, the invention adopts the following technical scheme:
an image side information estimation steganography method based on a super-resolution network comprises the following steps:
the following steps are carried out by a computer device,
amplifying the carrier image by using a super-resolution network to generate an estimation pre-carrier;
then, the estimation pre-carrier is sampled with floating point type precision to obtain an unquantized carrier, and the quantization error generated in the downsampling process can be estimated by subtracting the original carrier;
adjusting initial symmetric distortion to generate final asymmetric distortion by using the polarity guidance of the estimated quantization error;
finally, under the framework of minimizing distortion, the secret message is embedded by using an encoding technology to generate a secret image.
Further, the carrier comprises grey-scale spatial domain images of various sizes.
Further, the amplifying the carrier image by using the super-resolution network specifically includes:
and directly generating a corresponding high-resolution image, namely an estimated pre-carrier, according to the carrier image by using a super-resolution network, and respectively amplifying the width and the height of the image by 2 times.
Further, the generating of the estimation pre-carrier comprises inputting the carrier image into a trained super-resolution network, and the output high-resolution image is the estimation pre-carrier.
Further, the training steps of the super-resolution network are as follows:
the sample set is 10000 gray level image sets with the size of 512 multiplied by 512, and 256 multiplied by 256 gray level images are obtained by adopting standard bicubic interpolation functions in Matlab for the 512 multiplied by 512 images;
the super-resolution network was trained using 9900 512 x 512, 256 x 256 image pairs as high resolution images and low resolution images, and 100 additional image pairs were used to test the performance of the super-resolution network.
Further, the attention channel mechanism network RCAN is selected as the actually used super-resolution network structure.
Further, the estimation pre-carrier is subjected to downsampling with floating point type precision to obtain an unquantized carrier, wherein the downsampling with the floating point type precision is not subjected to rounding in the whole process, a standard bicubic interpolation function in Matlab is adopted to perform estimation pre-carrier downsampling on the image, and the unquantized carrier with the same size as the carrier image is obtained.
Further, adjusting the initial symmetric distortion to generate the final asymmetric distortion using the polarity guidance of the estimated quantization error comprises:
the distortion adjustment strategy for the spatial domain image side information estimation steganography is
And guiding distortion adjustment by using the estimated polarity direction of the quantization error, namely, in a modification direction consistent with the direction of the quantization error, giving the same adjustment coefficient to the initial distortion to reduce the modified distortion in the direction, and obtaining asymmetric distortion:
wherein the adjustment coefficient eta is belonged to [0,1 ].
In another aspect, the present invention also discloses a computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method as described above.
According to the technical scheme, the image side information estimation steganography method based on the super-resolution network guides the modification of the distortion function by adopting the super-resolution network to estimate the down-sampling side information, and the strategy for modifying the distortion only considers the polarity direction of the quantization error. The steganography scheme of the invention does not need to have an original carrier image, can obtain higher steganography safety and has wider application scenes.
Drawings
FIG. 1 is a flow chart of a conventional spatial domain image original side information steganography method;
fig. 2 is a flow chart of the image side information estimation steganography based on the super-resolution network of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
As shown in fig. 2, the image side information estimation steganography method based on the super-resolution network according to the embodiment of the present invention includes amplifying a carrier image by using the super-resolution network to generate an estimation pre-carrier. Then, the estimation pre-carrier is sampled with floating point type precision to obtain an unquantized carrier, and the original carrier is subtracted to estimate the quantization error generated in the downsampling process. Under the polarity adjustment strategy of the invention, the initial symmetric distortion is adjusted by using the polarity guidance of the quantization error obtained by estimation to generate the final asymmetric distortion. Finally, under the framework of minimizing distortion, the secret message is embedded by using the existing coding technology such as STC and the like to generate the secret-carrying image.
Each link in the flow chart is described in detail below:
1. carrier:
the carrier refers to gray-scale spatial domain images of various sizes.
2. Super-resolution network:
this link is the first key difference compared to the prior art. The prior art requires that the steganographer has an original pre-carrier and cannot be used without the original pre-carrier. The method directly generates a corresponding high-resolution image, namely the estimation pre-carrier, according to the carrier image by using the super-resolution network, and the method adopts the super-resolution network with the image width and the image height respectively amplified by 2 times, so that although the estimation pre-carrier is not as accurate as the original pre-carrier, effective side information can be extracted from the pre-carrier to improve the steganography safety.
Training a super-resolution network: the sample set is a gray image set BOSSBase 1.01 containing 10000 gray images with the size of 512 multiplied by 512, and 256 gray images of the 512 multiplied by 512 are obtained by adopting a standard bicubic interpolation function in Matlab. The super-resolution network was trained using 9900 512 x 512, 256 x 256 image pairs as high resolution images and low resolution images, and 100 additional image pairs were used to test the performance of the super-resolution network. Under the setting, compared with the safety of information steganography based on different super-resolution network estimation sides, the invention selects and uses the attention channel mechanism network RCAN as the actually used super-resolution network structure.
3. Estimating a pre-carrier:
and inputting the carrier image into a trained super-resolution network, wherein the output high-resolution image is the estimation pre-carrier.
4. Downsampling (floating point type precision):
the floating point type precision down sampling is that the whole process is not rounded, and the precision loss is reduced as much as possible. And (3) estimating pre-carrier down-sampling is carried out on the image by adopting a standard bicubic interpolation function in Matlab, so as to obtain an unquantized carrier with the same size as the carrier image.
5. Non-quantitative carrier
The same size as the carrier image, but each pixel is a floating point type value.
6. Quantization error:
and subtracting the corresponding pixel values of the unquantized carrier and the carrier image to obtain the quantization error of each pixel point.
7. Symmetric distortion:
the modification distortion of adding and subtracting one modification for each pixel point can be obtained through the existing traditional additive symmetric distortion functions, such as HILL and SUNIWARD, and the distortion of adding and subtracting one modification is equal at the moment.
8. Polar adjustment and asymmetric distortion:
the polar adjustment strategy is the second key difference between the present invention and the prior art, and the distortion adjustment strategy is different because the quantization errors generated by the estimated pre-carrier and the original pre-carrier are different in nature. The distortion adjustment strategy for spatial domain image side information estimation steganography in the invention is
And guiding distortion adjustment by using the estimated polarity direction of the quantization error, namely, in a modification direction consistent with the direction of the quantization error, giving the same adjustment coefficient to the initial distortion to reduce the modified distortion in the direction, and obtaining asymmetric distortion:
wherein the adjustment coefficient eta is belonged to [0,1 ].
9. Message embedding and message extraction:
the adjusted asymmetric distortion can be used to embed and extract the secret message using methods such as STC.
In another aspect, the present invention also discloses a computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method as described above.
It is understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, the example and the beneficial effects of the related contents can refer to the corresponding parts in the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. An image side information estimation steganography method based on a super-resolution network is characterized in that:
the following steps are carried out by a computer device,
amplifying the carrier image by using a super-resolution network to generate an estimation pre-carrier;
then, the estimation pre-carrier is sampled with floating point type precision to obtain an unquantized carrier, and the quantization error generated in the downsampling process can be estimated by subtracting the original carrier;
adjusting initial symmetric distortion to generate final asymmetric distortion by using the polarity guidance of the estimated quantization error;
finally, under the framework of minimizing distortion, the secret message is embedded by using an encoding technology to generate a secret image.
2. The super-resolution network-based image side-information estimation steganography method of claim 1, wherein: the carrier includes gray-scale spatial domain images of various sizes.
3. The super-resolution network-based image side-information estimation steganography method of claim 1, wherein: the amplifying the carrier image by using the super-resolution network specifically comprises the following steps:
and directly generating a corresponding high-resolution image, namely an estimated pre-carrier, according to the carrier image by using a super-resolution network, and respectively amplifying the width and the height of the image by 2 times.
4. The super-resolution network-based image side-information estimation steganography method of claim 1, wherein:
and the step of generating the estimation pre-carrier comprises the step of inputting the carrier image into a trained super-resolution network, wherein the output high-resolution image is the estimation pre-carrier.
5. The super-resolution network-based image side-information estimation steganography method of claim 4, wherein: the super-resolution network training comprises the following steps:
the sample set is 10000 gray level image sets with the size of 512 multiplied by 512, and 256 multiplied by 256 gray level images are obtained by adopting standard bicubic interpolation functions in Matlab for the 512 multiplied by 512 images;
the super-resolution network was trained using 9900 512 x 512, 256 x 256 image pairs as high resolution images and low resolution images, and 100 additional image pairs were used to test the performance of the super-resolution network.
6. The super-resolution network-based image side-information estimation steganography method of claim 4, wherein:
the use of the attention channel mechanism network RCAN is chosen as the actually used super-resolution network structure.
7. The super-resolution network-based image side-information estimation steganography method of claim 4, wherein:
and (3) downsampling the estimation pre-carrier with floating point type precision to obtain an unquantized carrier, wherein the downsampling with the floating point type precision is that the whole process is not rounded, and the estimation pre-carrier downsampling is carried out on the image by adopting a standard bicubic interpolation function in Matlab to obtain the unquantized carrier with the same size as the carrier image.
8. The super-resolution network-based image side-information estimation steganography method of claim 1, wherein: adjusting the initial symmetric distortion to generate final asymmetric distortion using the estimated polarity guidance of the quantization error, comprising:
the distortion adjustment strategy for the spatial domain image side information estimation steganography is
And guiding distortion adjustment by using the estimated polarity direction of the quantization error, namely, in a modification direction consistent with the direction of the quantization error, giving the same adjustment coefficient to the initial distortion to reduce the modified distortion in the direction, and obtaining asymmetric distortion:
wherein the adjustment coefficient eta is belonged to [0,1 ].
9. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 8.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6557103B1 (en) * | 1998-04-13 | 2003-04-29 | The United States Of America As Represented By The Secretary Of The Army | Spread spectrum image steganography |
| CN111461973A (en) * | 2020-01-17 | 2020-07-28 | 华中科技大学 | Super-resolution reconstruction method and system for image |
| CN111614964A (en) * | 2020-05-29 | 2020-09-01 | 中国科学技术大学 | Non-additive steganography method for enhancing JPEG image block boundary continuity |
-
2021
- 2021-06-28 CN CN202110720997.8A patent/CN113436054A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6557103B1 (en) * | 1998-04-13 | 2003-04-29 | The United States Of America As Represented By The Secretary Of The Army | Spread spectrum image steganography |
| CN111461973A (en) * | 2020-01-17 | 2020-07-28 | 华中科技大学 | Super-resolution reconstruction method and system for image |
| CN111614964A (en) * | 2020-05-29 | 2020-09-01 | 中国科学技术大学 | Non-additive steganography method for enhancing JPEG image block boundary continuity |
Non-Patent Citations (4)
| Title |
|---|
| WEIXIANG LI等: "JPEG Steganography With Estimated Side-Information", 《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》, vol. 30, no. 7, pages 2288, XP011796764, DOI: 10.1109/TCSVT.2019.2925118 * |
| YULUN ZHANG等: "Image Super-Resolution Using Very Deep Residual Channel Attention Networks", 《ARXIV》, 12 July 2018 (2018-07-12), pages 1 - 16 * |
| 王丽娜;王博;翟黎明;徐一波;: "基于边信息估计的JPEG图像隐写方法", 华南理工大学学报(自然科学版), no. 05, pages 9 - 15 * |
| 王博: "基于边信息估计的图像隐写方法研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, no. 6, pages 138 - 50 * |
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