CN111327786B - Robust steganography method based on social network platform - Google Patents

Robust steganography method based on social network platform Download PDF

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CN111327786B
CN111327786B CN202010118578.2A CN202010118578A CN111327786B CN 111327786 B CN111327786 B CN 111327786B CN 202010118578 A CN202010118578 A CN 202010118578A CN 111327786 B CN111327786 B CN 111327786B
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CN111327786A (en
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张卫明
俞能海
于心智
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University of Science and Technology of China USTC
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
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    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
    • H04N1/32267—Methods relating to embedding, encoding, decoding, detection or retrieval operations combined with processing of the image
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
    • H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
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    • H04N1/32165—Transform domain methods using cosine transforms
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    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
    • H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
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Abstract

The invention discloses a robust steganography method based on a social network platform, which comprises the following steps: for a given carrier image X, calculating the symmetric distortion of the carrier image X by using a steganography algorithm, and adjusting by using an asymmetric distortion frame to obtain asymmetric distortion; for a given carrier image X, extracting carrier elements from a specified embedded domain, and calculating modification distances of the carrier elements according to a generalized jitter modulation mechanism; obtaining the modified distortion of the carrier element by combining the asymmetric distortion and the modified distance of the carrier element; and based on the modification distortion of the carrier element, embedding the coded secret information into the carrier image with the modification distortion to obtain the secret image. The method can be suitable for a real social network platform scene, meets the requirements in practical application, and has good performance in the aspects of safety, robustness and capacity.

Description

Robust steganography method based on social network platform
Technical Field
The invention relates to the technical field of social network security and steganography, in particular to a robust steganography method based on a social network platform.
Background
Steganography is a technology of covert communication, and aims to realize covert communication by using digital multimedia (such as images) as a carrier to cover secret information. Because the complex texture region of the image is difficult to model, the fluctuation caused by the high-dimensional statistical features of the image is small when the complex texture region is modified, and therefore high safety performance can be achieved when the complex texture region of the image is modified. At present, the mainstream adaptive steganography technology is to reasonably define a distortion function based on image texture and further utilize the minimized distortion steganography STC to achieve high security. In order to further improve the security of the steganography algorithm, Zichi Wang et al adjust a distortion function by estimating JPEG image side information to obtain asymmetric distortion, thereby greatly improving the security of steganography.
Although current steganographic algorithms can achieve high security, they are not applicable to practical social networking platforms. Due to the limitations of memory and bandwidth, the social network platform performs lossy processing on the uploaded image, such as JPEG recompression, and the current steganographic algorithm performs laboratory environment communication based on an ideal channel, so that the current steganographic algorithm cannot be applied to the actual social network platform. With the rapid development and popularization of intelligent mobile terminals, sharing images on a social network platform gradually becomes a popular living habit, which provides a favorable application environment for covert communication, and therefore, designing a steganographic algorithm applicable to the social network platform is urgent. Different from a laboratory environment steganography algorithm based on an ideal channel, the steganography algorithm based on a lossy channel is called a robust steganography algorithm.
Because the main lossy processing mode of the mainstream social network platform on the uploaded image is JPEG recompression, the current robust steganography mainly aims at JPEG recompression expansion. In 2016, Yi Zhang proposes a robust steganography framework based on the construction of an anti-compression domain and the combination of RS and STC coding, and the framework obtains stronger robustness by constructing the anti-compression domain and utilizing RS error correction codes. They then propose a robust steganographic algorithm based on the relative relationship between four adjacent DCT coefficients. In order to fully utilize the characteristics of quantization operation, Yi Zhang et al propose a DMAS robust steganography algorithm by modifying the intermediate frequency DCT coefficients of JPEG images by using the idea of dither modulation, wherein the algorithm can obtain stronger robust performance, but has poorer safety and capacity. In 2018, Jinyuan Tao proposed an indirectly modified robust steganography algorithm based on a simplified JPEG recompression process, and although the algorithm can theoretically achieve zero bit error rate, the Jinyuan Tao cannot be applied to an actual social network platform because a real JPEG recompression process is not considered. In order to reduce the influence of the social network platform, the Zengzhen Zhao uploads and downloads the carrier for multiple times through the social network platform in advance so that the carrier image is adapted to the social channel, and then stronger robustness is obtained by combining an error correction code, but the behavior violates the nature of steganography, namely, a behavior safety criterion.
As described above, the existing robust steganographic algorithms have respective problems in terms of security, capacity, or practical application capability, etc. only for making the steganographic algorithms robust. In order to meet the requirements in practical application, the robust steganographic algorithm must have good performance in terms of security, robustness and capacity. Therefore, it is necessary to provide a robust steganography algorithm that can be applied to real social network platform scenarios.
Disclosure of Invention
The invention aims to provide a robust steganography method based on a social network platform, which can be suitable for a real social network platform scene.
The purpose of the invention is realized by the following technical scheme:
a robust steganography method based on a social network platform comprises the following steps:
for a given carrier image X, calculating the symmetric distortion of the carrier image X by using the existing steganography algorithm, and adjusting by using an asymmetric distortion frame to obtain asymmetric distortion;
for a given carrier image X, extracting carrier elements from a specified embedded domain, and calculating modification distances of the carrier elements according to a generalized jitter modulation mechanism;
obtaining the modified distortion of the carrier element by combining the asymmetric distortion and the modified distance of the carrier element;
and based on the modification distortion of the carrier element, embedding the coded secret information into the carrier image to obtain the carrier image.
The technical scheme provided by the invention can meet the requirements in practical application and has better performance in the aspects of safety, robustness and capacity.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a robust steganography method based on a social network platform according to an embodiment of the present invention;
FIG. 2 is a flow chart for obtaining asymmetric distortion according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating designated anti-compression domains provided by an embodiment of the present invention;
FIG. 4 is a diagram illustrating a generalized dithering modulation scheme according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an experimental result provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are 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 only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a robust steganography method based on a social network platform, which mainly comprises the following steps of:
1. for a given JPEG carrier image X, calculating the symmetric distortion of the carrier image X by using a steganographic algorithm, and adjusting by using an asymmetric distortion frame to obtain asymmetric distortion.
In the embodiment of the invention, the carrier image takes a JPEG image as an example, and the invention can be applied after other format images are converted into JPEG format images.
As shown in fig. 2, in the embodiment of the present invention, an improved asymmetric distortion framework is used, and the steganography algorithm may use an existing algorithm, for example, an existing distortion function (e.g., J-UNIWARD, UERD, etc.).
The preferred embodiment of this step is as follows:
1) for a given carrier image X, its quantization table Q is obtained.
2) Calculating the symmetric distortion rho of the carrier image X by using a steganographic algorithm, and decompressing the carrier image X to a space domain to obtain a space domain image J-1(X)。
3) Using the mean filter F to the spatial domain image J-1(X) filtering to obtain a filtered spatial domain image S (smoothed spatial domain image):
Figure GDA0003152978470000031
illustratively, the mean filter F may be of the form:
Figure GDA0003152978470000032
4) converting the filtered spatial domain image S into DCT domain by DCT transform to obtain non-quantized DCT coefficient
Figure GDA0003152978470000033
5) Using non-quantized DCT coefficients
Figure GDA0003152978470000041
Calculating asymmetric distortion rho from symmetric distortion rho+And ρ-：
Figure GDA0003152978470000042
Figure GDA0003152978470000043
Where the subscript ij denotes the (i, j) position in the carrier image.
In the embodiment of the invention, capital letters correspond to lowercase letters, for example, X represents a carrier image, namely an X tableAll coefficients of the carrier image, then xijIs the coefficient at the (i, j) position in the carrier image; q represents a quantization table, Q represents a quantization step size at any position, and Q represents a quantization step size at any positionijIt represents the quantization step size at the (i, j) position in the carrier image.
Figure GDA0003152978470000044
Representing non-quantized DCT coefficients, then
Figure GDA0003152978470000045
Representing the non-quantized DCT coefficients at the (i, j) position in the carrier image; d represents a non-quantized DCT coefficient matrix, D in the following formula represents a non-quantized DCT coefficient at an arbitrary position, DijIt represents the non-quantized DCT coefficients at the (i, j) position. Rho+、ρ-Positive and negative asymmetric distortions, respectively, then ρij +、ρij -Corresponding to the positive and negative polarity asymmetric distortions at the (i, j) position in the carrier image.
2. For a given carrier image X, carrier elements are extracted from the specified embedded domain, and the modification distances of the carrier elements are calculated according to a generalized dither modulation scheme.
Taking fig. 3 as an example, an example of carrier element extraction from a compression-resistant domain is shown.
The preferred embodiment of this step is as follows:
1) obtaining a non-quantization DCT coefficient matrix D of the carrier image X, and selecting a standard quantization table according to the social network platform environment
Figure GDA0003152978470000046
Standard quantization table
Figure GDA0003152978470000047
As long as the corresponding quality factor is not greater than the quality factor corresponding to the social network platform JPEG recompression.
2) Calculating the positive polarity modification distance h of the carrier element according to the generalized jitter modulation mechanism+And negative polarity modification distance h-；
In the generalized jitter modulation mechanism, non-quantized DCT coefficients of carrier elements are quantized according to a selected quantization step size
Figure GDA0003152978470000048
Dividing into odd class and even class, respectively representing message bits 1 and 0, and using standard quantization table
Figure GDA0003152978470000049
Realizing message embedding in the quantization process of the non-quantization DCT coefficient d;
positive polarity modification distance h+And negative polarity modification distance h-The calculation formula of (2) is as follows:
Figure GDA00031529784700000410
Figure GDA00031529784700000411
where k is a natural number, subscript ij denotes the (i, j) position in the carrier image X, dijRepresenting the non-quantized DCT coefficients at the (i, j) position in the carrier image X,
Figure GDA0003152978470000051
each being a positive polarity modification distance, a negative polarity modification distance of the carrier element at the (i, j) position in the carrier image X.
It should be noted that, in the embodiment of the present invention, the execution sequence of the step 1 and the step 2 is not limited, and the steps may be executed synchronously, or executed in tandem (for example, execute the step 1 and then execute the step 2, or execute the step 2 and then execute the step 1).
3. The modified distortion of the carrier element is obtained by combining the asymmetric distortion with the modified distance of the carrier element.
Taking FIG. 4 as an example, the positive polarity modified distortion is ξ+＝ρ+/q×h+Negative polarity modified distortion of xi-＝ρ-/q×h-(where q is the quantization step corresponding to the carrier X), and is specifically countedThe calculation formula is as follows:
Figure GDA0003152978470000052
Figure GDA0003152978470000053
wherein h is+And h-Indicating the modification distance and the index ij indicates the (i, j) position in the carrier image.
To minimize the embedding distortion, the proposed modification rule is:
Figure GDA0003152978470000054
wherein the content of the first and second substances,
Figure GDA0003152978470000055
is the DCT coefficient corresponding to the secret image. For any carrier element dijWherein
Figure GDA0003152978470000056
4. And embedding the coded secret information into the carrier image based on the modification distortion of the carrier element to obtain the carrier image.
The original secret information m is coded by an error correction code to obtain coded secret information
Figure GDA0003152978470000057
Illustratively, the original secret information m may be encoded using an RS (31,15) error correction code.
Based on modification distortion of carrier elements, encoding the encoded secret information by using ternary STCs (space time coding)
Figure GDA0003152978470000058
And embedding the image into a carrier image to obtain a secret carrier image Y.
The embodiment of the invention also comprises the following steps: the method for decoding the secret-carrying image by the receiving party comprises the following steps:
when the secret information is extracted at the receiving end, the receiving end receives the secret-carrying image obtained by recompressing the secret-carrying image Y by the social network platform
Figure GDA0003152978470000059
The receiver obtains the secret-carrying image by calculation
Figure GDA00031529784700000510
Then using a standard quantization table agreed with the sender
Figure GDA00031529784700000511
Extracting information to obtain secret information coded by error correcting code
Figure GDA00031529784700000512
The bit information represented by each secret-carrying element is the bit information represented by the odd-class or even-class interval in which the secret-carrying element is located, and then the original secret information m can be obtained by decoding the bit information by using an error correcting code (for example, RS (31, 15)).
In order to illustrate the effects of the above-described scheme of the embodiment of the present invention, a related experiment was also performed. The results of the experiment are shown in FIG. 5.
The performance of the robust steganography method based on the social network platform and the robust steganography method proposed by Zhang Yi et al are shown in FIG. 5. The robust steganography method proposed by Zhang Yi et al mainly combines the traditional jitter modulation with binary STCs coding for message embedding, and because only the intermediate frequency region is used as an embedded domain and the binary STCs coding efficiency is low, the security and the capacity are relatively poor.
The left graph in fig. 5 is a safety performance comparison of the two methods, and the experimental setup is: experiments are carried out by using a BOSSbase1.01 image library with a quality factor of 75, steganalysis is characterized by CCPEV (PEV features enhanced by healthcare) calibration, embedding rate is 0.05bpnzac to 0.15bpnzac, 5000 images in the image library are used for training a classifier, 5000 images are used for testing, and 10 experiments are carried out to obtain average detection error rate.
The right graph in fig. 5 is a comparison of the robustness of the two methods, and the experimental settings are as follows: 1000 images are randomly selected from a BOSSbase1.01 image library with the quality factor of 65 for experiment, the quality factor of channel compression is set to be 95, and the average error rate of the 1000 images is taken.
Therefore, the effect of the scheme of the embodiment of the invention is obviously better than that of the existing scheme.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A robust steganography method based on a social network platform is characterized by comprising the following steps:
for a given carrier image X, calculating the symmetric distortion of the carrier image X by using the existing steganography algorithm, and adjusting by using an asymmetric distortion frame to obtain asymmetric distortion;
for a given carrier image X, extracting carrier elements from a specified embedded domain, and calculating modification distances of the carrier elements according to a generalized jitter modulation mechanism;
obtaining the modified distortion of the carrier element by combining the asymmetric distortion and the modified distance of the carrier element;
and based on the modification distortion of the carrier element, embedding the coded secret information into the carrier image to obtain the carrier image.
2. The robust steganography method based on the social network platform as claimed in claim 1, wherein the calculating the symmetric distortion of the carrier image by using the existing steganography algorithm, and the obtaining the asymmetric distortion by the asymmetric distortion framework adjustment comprises:
for a given carrier image X, obtaining a quantization table Q thereof;
calculating the symmetric distortion rho of the carrier image X by using a steganographic algorithm, and decompressing the carrier image X to a space domain to obtain a space domain image J-1(X)；
Using the mean filter F to the spatial domain image J-1(X) filtering to obtain a filtered spatial domain image S:
Figure FDA0003188510990000011
converting the filtered spatial domain image S into DCT domain by DCT transform to obtain non-quantized DCT coefficient
Figure FDA0003188510990000012
Using non-quantized DCT coefficients
Figure FDA0003188510990000013
Calculating asymmetric distortion rho from symmetric distortion rho+And ρ-：
Figure FDA0003188510990000014
Figure FDA0003188510990000015
Wherein the subscript ij denotes the (i, j) position in the carrier image X, XijIs the coefficient at the (i, j) position in the carrier image, qijThen represents the quantization step size at the (i, j) position in the carrier image,
Figure FDA0003188510990000016
representing the unquantized DCT coefficients at the (i, j) position in the carrier image, p+、ρ-Asymmetric distortion of positive and negative polarity, respectively, rhoij +、ρij -Representing positive and negative polarity asymmetric distortions at the (i, j) position in the carrier image.
3. The social network platform-based robust steganography method according to claim 1, wherein the calculating the modification distance of the carrier element according to the generalized jitter modulation scheme comprises:
obtaining a non-quantization DCT coefficient matrix D of the carrier image X, and selecting a standard quantization table according to the social network platform environment
Figure FDA0003188510990000021
Then calculating the positive polarity modification distance h of the carrier element according to a generalized jitter modulation mechanism+And negative polarity modification distance h-；
In the generalized jitter modulation mechanism, non-quantization DCT coefficients of carrier elements are subjected to quantization step size selection
Figure FDA0003188510990000022
Dividing into odd class and even class, respectively representing message bits 1 and 0, and using standard quantization table
Figure FDA0003188510990000023
Realizing message embedding in the quantization process of the non-quantization DCT coefficient d;
positive polarity modification distance h+And negative polarity modification distance h-The calculation formula of (2) is as follows:
Figure FDA0003188510990000024
Figure FDA0003188510990000025
where k is a natural number, subscript ij denotes the (i, j) position in the carrier image X, dijRepresenting the non-quantized DCT coefficients at the (i, j) position in the carrier image X,
Figure FDA0003188510990000026
each being a positive polarity modification distance, a negative polarity modification distance of the carrier element at the (i, j) position in the carrier image X.
4. The social network platform-based robust steganography method according to claim 1, wherein the obtaining of the modified distortion of the carrier element by combining the asymmetric distortion and the modified distance of the carrier element comprises: positive polarity modified distortion xi+Distortion xi modified by negative polarity-：
Positive polarity modified distortion to xi+＝ρ+/q×h+Negative polarity modified distortion of xi-＝ρ-/q×h-The specific calculation formula is as follows:
Figure FDA0003188510990000027
Figure FDA0003188510990000028
wherein h is+And h-Respectively representing the positive and negative modification distances, the subscript ij representing the (i, j) position in the carrier image,
Figure FDA0003188510990000029
respectively representing the positive polarity modification distance and the negative polarity modification distance of the carrier element at the (i, j) position in the carrier image; rho+、ρ-Respectively representing positive and negative asymmetric distortions, pij +、ρij -Respectively representing positive and negative asymmetric distortions at the (i, j) position in the carrier image, qijIt represents the quantization step size at the (i, j) position in the carrier image.
5. The robust steganography method based on a social networking platform of claim 1, wherein the first and second sets of data are stored in a memory,
the original secret information m is coded by an error correction code to obtain coded secret information
Figure FDA00031885109900000210
Based on modification distortion of carrier elements, encoding the encoded secret information by using ternary STCs (space time coding)
Figure FDA00031885109900000211
And embedding the image into a carrier image to obtain a secret carrier image Y.
6. The robust steganography method based on a social network platform as claimed in claim 1, wherein the method further comprises: the method for decoding the secret-carrying image by the receiving party comprises the following steps:
the receiver receives the secret-carrying image which is obtained by recompressing the secret-carrying image Y by the social network platform
Figure FDA0003188510990000031
Obtaining secret-carrying image by calculation
Figure FDA0003188510990000032
Then using a standard quantization table agreed with the sender
Figure FDA0003188510990000033
Extracting information to obtain secret information coded by error correcting code
Figure FDA0003188510990000034
The bit information represented by each secret-carrying element is the bit information represented by the odd-class or even-class interval in which the secret-carrying element is positioned, and then the original secret information m can be obtained by decoding by using an error correcting code.
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