WO2020093769A1 - Procédé et dispositif permettant d'intégrer des informations dans une image - Google Patents

Procédé et dispositif permettant d'intégrer des informations dans une image Download PDF

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WO2020093769A1
WO2020093769A1 PCT/CN2019/103578 CN2019103578W WO2020093769A1 WO 2020093769 A1 WO2020093769 A1 WO 2020093769A1 CN 2019103578 W CN2019103578 W CN 2019103578W WO 2020093769 A1 WO2020093769 A1 WO 2020093769A1
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matrix
information
frequency domain
coefficient
data
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PCT/CN2019/103578
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Chinese (zh)
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韩胜辉
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京东数字科技控股有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/46—Embedding additional information in the video signal during the compression process

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  • the embodiments of the present application relate to the field of computer technology, specifically to the field of image processing technology, and in particular, to a method and device for embedding information in a picture.
  • the embodiment of the present application proposes a method and device for embedding information in a picture.
  • an embodiment of the present application provides a method for embedding information in a picture.
  • the method includes: using a first transformation matrix to convert a space domain data matrix of a screenshot into an original frequency domain coefficient matrix; The matrix is rounded, quantized, and rounded to obtain the intermediate frequency domain coefficient matrix, where the rounding and the rounding respectively retain the integer part of the source data, and remove the fractional part of the source data; embed the information to be embedded Go to the DC coefficient of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information; use the second transformation matrix to convert the frequency domain coefficient matrix of the embedded information into the spatial data matrix of the embedded information; based on the embedded The spatial data matrix of information generates pictures with embedded information.
  • the data in the spatial data matrix of the screenshot includes brightness data and color data.
  • embedding the information to be embedded into the DC coefficients of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information includes: in response to determining the DC coefficients, the remainder after modulo 2 and the information to be embedded The bits are not the same, the sign bit of the DC coefficient remains unchanged, and the value of the DC coefficient value bit is subtracted by 1 to obtain the frequency domain coefficient matrix of the embedded information.
  • embedding the information to be embedded into the DC coefficients of the intermediate frequency domain coefficient matrix to obtain the frequency information coefficient matrix of the embedded information includes: converting the DC coefficients into binary data expressed in the form of original codes; responding to It is determined that the lowest bit of the DC coefficient is not the same as the information bit to be embedded, the sign bit of the DC coefficient is kept unchanged, and the value of the value bit of the DC coefficient is subtracted by 1 to obtain the frequency domain coefficient matrix of the embedded information.
  • the first transform matrix is used to convert the spatial domain data matrix of the screenshot to the original frequency domain coefficient matrix, which includes: matrix segmentation of the spatial domain data matrix of the screenshot; the discrete Fourier transform matrix is used for each A matrix block is converted, and the spatial data matrix of the screenshot is converted into the original frequency domain coefficient matrix.
  • the first transformation matrix is used to convert the screenshot spatial domain data matrix to the original frequency domain coefficient matrix, which includes: performing matrix division of the screenshot spatial domain data matrix; using a discrete cosine transform matrix, for each matrix The block is converted, and the spatial data matrix of the screenshot is converted into the original frequency domain coefficient matrix.
  • the second transform matrix is used to convert the frequency domain coefficient matrix of the embedded information into the spatial data matrix of the embedded information, including: using a quantization table to inverse the frequency domain coefficient matrix of the embedded information
  • the inverse transform matrix of the discrete Fourier transform is used to convert the frequency domain coefficient matrix of the embedded information after inverse quantization into the spatial data matrix of the embedded information.
  • the second transform matrix is used to convert the frequency domain coefficient matrix of the embedded information into the spatial data matrix of the embedded information, including: using a quantization table to inverse the frequency domain coefficient matrix of the embedded information
  • Inverse discrete cosine transform matrix is used to convert the inversely quantized frequency domain coefficient matrix of the embedded information into the spatial domain data matrix of the embedded information.
  • performing matrix division on the captured spatial domain data matrix includes: performing 4 * 4 or 8 * 8 or 16 * 16 matrix division on the captured spatial domain data matrix.
  • an embodiment of the present application provides an apparatus for embedding information in a picture.
  • the apparatus includes: a coefficient matrix conversion unit configured to convert the spatial domain data matrix of the screenshot into an original frequency domain coefficient matrix; a coefficient matrix optimization unit , Configured to perform one rounding, quantization, and second rounding on the original frequency domain coefficient matrix to obtain an intermediate frequency domain coefficient matrix, where the first rounding and the second rounding respectively retain the integer part of the source data, and remove the source data The fractional part of; the matrix information embedding unit, configured to embed the information to be embedded into the DC coefficients of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information; the data matrix conversion unit, configured to embed the embedded information The frequency domain coefficient matrix of is converted into a spatial data matrix with embedded information; the information picture generating unit is configured to generate a picture with embedded information based on the spatial data matrix with embedded information.
  • the data in the spatial domain data matrix captured in the coefficient matrix conversion unit includes brightness data and color data.
  • the matrix information embedding unit is further configured to: in response to determining that the remainder after the DC coefficient is modulo 2 is not the same as the information bit to be embedded, leave the sign bit of the DC coefficient unchanged, The value of is subtracted by 1 to obtain the frequency domain coefficient matrix with embedded information.
  • the matrix information embedding unit includes: a data conversion module configured to convert the DC coefficient into binary data expressed in original code; an information embedding module configured to respond to the determination of the lowest bit of the DC coefficient and the pending The embedded information bits are not the same, the sign bit of the DC coefficient remains unchanged, and the value of the DC coefficient value bit is subtracted by 1 to obtain the frequency domain coefficient matrix of the embedded information.
  • the coefficient matrix conversion unit includes: a first matrix division module configured to perform matrix division on the screenshot spatial domain data matrix; a discrete Fourier transform module configured to use the discrete Fourier transform matrix, Transform each matrix block, and transform the spatial data matrix of the screenshot into the original frequency domain coefficient matrix.
  • the coefficient matrix conversion unit includes: a second matrix division module configured to perform matrix division on the spatial data matrix of the screenshot; a discrete cosine transformation module configured to use the discrete cosine transformation matrix for each matrix The block is converted, and the spatial data matrix of the screenshot is converted into the original frequency domain coefficient matrix.
  • the data matrix conversion unit includes: a first inverse quantization module configured to inversely quantize the frequency domain coefficient matrix of the embedded information using a quantization table; an inverse transform module of discrete Fourier transform, It is configured to use the inverse transform matrix of discrete Fourier transform to convert the inversely quantized frequency domain coefficient matrix of the embedded information into the spatial domain data matrix of the embedded information.
  • the first matrix segmentation module is further configured to: perform 4 * 4 or 8 * 8 or 16 * 16 matrix segmentation on the screenshot spatial domain data matrix.
  • the data matrix conversion unit includes: a second inverse quantization module configured to use a quantization table to inversely quantize the frequency domain coefficient matrix of the embedded information; an inverse discrete cosine transform module configured to use The inverse discrete cosine transform matrix converts the inversely quantized frequency domain coefficient matrix of the embedded information into the spatial domain data matrix of the embedded information.
  • the second matrix segmentation module is further configured to: perform a 4 * 4 or 8 * 8 or 16 * 16 matrix segmentation on the captured spatial domain data matrix.
  • an embodiment of the present application provides an electronic device including: one or more processors; a storage device on which one or more programs are stored; when one or more programs are Executed by one processor, so that one or more processors implement the method as described in any one of the implementation manners of the first aspect.
  • an embodiment of the present application provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, a method as described in any implementation manner of the first aspect is implemented.
  • the method and device for embedding information in a picture provided by the embodiments of the present application, by converting the picture from the spatial domain to the frequency domain, the frequency domain coefficients are rounded, quantized, and rounded once.
  • the second rounding separately retains the integer part of the source data, removes the decimal part of the source data, and then embeds the information to be embedded into the DC coefficient, and finally converts the picture from the frequency domain to the spatial domain to generate a picture with embedded information.
  • the above-mentioned first rounding and second rounding can avoid data overflow errors and effectively embed information into pictures.
  • signal processing processes such as lossy compression, low-pass filtering, sub-sampling, interpolation, etc.
  • the protection of the DC coefficient is better than that of the AC coefficient.
  • the protection is better to avoid the loss of embedded information during transmission.
  • the information embedded in the DC coefficient is more robust during signal processing.
  • FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;
  • FIG. 2 is a flowchart of an embodiment of a method for embedding information in a picture according to the present application
  • FIG. 3 is a schematic diagram of an application scenario of a method for embedding information in a picture according to the present application
  • FIG. 4 is a flowchart of another embodiment of a method for embedding information in a picture according to the present application.
  • FIG. 5 is a schematic structural diagram of an embodiment of an apparatus for embedding information in a picture according to the present application
  • FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a terminal device according to an embodiment of the present application.
  • FIG. 1 shows an exemplary system architecture 100 of an embodiment of an embodiment of a method for embedding information in a picture and a device for embedding information in a picture of the present application.
  • the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105.
  • the network 104 is a medium used to provide a communication link between the terminal devices 101, 102, 103 and the server 105.
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, and so on.
  • the user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, and so on.
  • Various client applications may be installed on the terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, and social platform software.
  • the terminal devices 101, 102, and 103 may be hardware or software.
  • the terminal devices 101, 102, and 103 may be various electronic devices with display screens and screenshots, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. Wait.
  • the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example to provide distributed services), or as a single software or software module. There is no specific limit here.
  • the server 105 may be a server that provides various services, for example, a client server that supports clients running on the terminal devices 101, 102, and 103.
  • the client server can customize the information to be embedded, and feed back customization requirements (for example, requirements for customizing network status information) to the terminal device, and the terminal device provides the information to be embedded.
  • the client server may also provide the information to be embedded, and send the information to the terminal device (for example, the information to be embedded is copyright flag information set by the client server).
  • the method for embedding information in pictures provided by the embodiments of the present application is generally performed by terminal devices 101, 102, and 103, and accordingly, the device for embedding information in pictures is generally provided in terminal devices 101, 102, and 103. in.
  • terminal devices, networks, and servers in FIG. 1 are only schematic. According to the implementation needs, there can be any number of terminal devices, networks and servers.
  • the method of embedding information in the picture includes the following steps:
  • step 201 the first transformation matrix is used to convert the space domain data matrix of the screenshot to the original frequency domain coefficient matrix.
  • the execution subject of the method of embedding information in the picture may use the first transformation matrix to convert the spatial domain data matrix of the screenshot to the original frequency domain coefficient matrix.
  • the spatial domain also known as image space, is a space composed of image pixels.
  • length is used as an independent variable to directly process pixel data.
  • the spatial domain data matrix is a matrix obtained by storing pixel data of each pixel according to the correspondence between each pixel and the coordinate position.
  • the above pixel data may include, but is not limited to, pixel data in RGB format, YUV format, and pixel data in other formats now known or developed in the future.
  • the pixel data in the YUV format may include, but is not limited to, pixel data in the Y'UV format, YUV format, YCbCr format, YPbPr format, and pixel data in other formats that are now known or developed in the future.
  • the frequency domain is an area that describes the characteristics of an image with frequency as its independent variable. It can decompose the spatial change of pixel data of an image into a linear superposition of simple vibration functions with different amplitudes, frequencies and phases. Frequency, amplitude, and phase describe all the information in the sinusoidal image.
  • the amplitude of the image characterizes the difference between the brightest and darkest peaks in the image, and the phase characterizes the offset of the waveform relative to the original waveform.
  • the frequency is an indicator of the intensity of the grayscale change in the image. Gradient of grayscale in plane space. The higher the frequency, the faster the original signal changes, and the lower the frequency, the smoother the original signal.
  • some masking characteristics of the human perception system can be more easily integrated into the process of information embedding. Decomposing and processing the frequency coefficients of the image in the frequency domain can effectively embed the information to be embedded into the picture .
  • the spatial and frequency domains of the image can be converted to each other. You can use the already mature transform domain technology to convert the image from the spatial domain to the frequency domain.
  • the first transform matrix When the first transform matrix is used to convert the captured spatial domain data matrix to the original frequency domain coefficient matrix, existing and future developed transform domain technologies that convert the image from the spatial domain to the frequency domain can be used.
  • using the first transformation matrix to convert the spatial domain data matrix of the screenshot to the original frequency domain coefficient matrix may include: performing matrix division of the spatial domain data matrix of the screenshot; using a discrete Fourier transform matrix, Convert the image from image space to frequency domain space, that is, transform the spatial data matrix of the screenshot into the original frequency domain coefficient matrix.
  • the above-mentioned transformation using discrete Fourier transform matrix can eliminate the spatial redundancy of the image and reduce the amount of calculation.
  • using the first transformation matrix to convert the spatial domain data matrix of the screenshot to the original frequency domain coefficient matrix may include: matrix partitioning the spatial domain data matrix of the screenshot; using a discrete cosine transform matrix, the The image is converted from image space to frequency domain space, that is, the spatial data matrix of the screenshot is converted into the original frequency domain coefficient matrix.
  • the amount of calculation is reduced by half, which improves the operation efficiency, and has a good decorrelation performance, which can effectively eliminate the image space Redundancy helps to further entropy encode the image.
  • the correlation means that since the changes between the pixels of the image are smoothly changed, each pixel and the adjacent pixels are related, which is not conducive to entropy encoding the image.
  • a 4 * 4 matrix division method may be used to perform matrix division on the spatial data matrix of the screenshot.
  • the use of the 4 * 4 matrix division method described above can reduce the complexity of the algorithm for converting the spatial domain and the frequency domain of the image, and improve the efficiency of the transform domain calculation.
  • an 8 * 8 matrix division method may be used to perform matrix division on the spatial data matrix of the screenshot.
  • the above 8 * 8 matrix division method can reduce the complexity of the algorithm of the spatial and frequency domain conversion of the image, improve the efficiency of the transform domain operation and reduce the image blocking effect, and ensure the continuity of the boundary of the block in the reconstructed image.
  • the above-mentioned image blocking effect means that the image has discontinuous defects at the boundary of the block.
  • a 16 * 16 matrix division method may be used to perform matrix division on the spatial data matrix of the screenshot.
  • the above 16 * 16 matrix segmentation method can significantly reduce the image blocking effect, ensure the continuity of the block boundaries in the reconstructed image, and improve the image fidelity.
  • Step 202 Perform primary rounding, quantization, and secondary rounding on the original frequency domain coefficient matrix to obtain an intermediate frequency domain coefficient matrix, where the primary rounding and secondary rounding respectively retain the integer part of the source data, and remove the source data. decimal part.
  • the above-mentioned execution subject may round the original frequency domain coefficient matrix once, where the rounding reserves the source once
  • the integer part of the data excluding the decimal part of the source data.
  • the source data rounded at one time is 75.9, then after one rounding, the data becomes 75. Ensure that the image pixel data does not exceed the critical value during the conversion of the spatial domain and the frequency domain to avoid the loss of embedded data.
  • Quantization is the operation of reducing the bit rate through the mapping relationship, that is, dividing the data in the original frequency domain coefficient matrix by the quantization coefficient at the corresponding position in the quantization table.
  • the quantization process is actually an optimization process for the frequency domain coefficients. It takes advantage of the insensitivity of the human eye to high-frequency components to achieve a substantial simplification of data.
  • quantization tables for example, the standard 8 * 8 quantization table used in the JPEG compression encoder shown in Table 1:
  • the original frequency domain coefficient matrix after rounding and quantization is rounded twice to obtain an intermediate frequency domain coefficient matrix, in which the rounding preserves the integer part of the source data and removes the fractional part of the source data.
  • the source data of the second rounding is 65.7, then after the second rounding, the data becomes 65.
  • the above rounding operation can ensure that the image pixel data does not exceed the critical value during the conversion of the spatial domain and the frequency domain, and avoid the loss of embedded data.
  • the above-mentioned first rounding and second rounding can avoid data overflow errors and ensure that the embedded information is more robust.
  • the source data is RGB format data, and its value is 255.
  • the integer part of the source data is retained, and the fractional part of the source data is removed.
  • Rounding operation instead of rounding, can avoid data overflow error when the image pixel data corresponding to the frequency coefficient exceeds the threshold range (that is, the RGB value exceeds 255) when the image is converted from the frequency domain to the spatial domain To effectively embed information into pictures.
  • Step 203 Embed the information to be embedded into the DC coefficient of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information.
  • the execution body of the method of embedding information in the picture may store the information to be embedded in advance, for example, the above-mentioned information to be embedded may be information such as model information, CPU information, etc. of the execution body.
  • the information to be embedded may also be information generated by the execution subject according to a preset rule, for example, the information may be information such as network status information and memory information of the execution subject when taking a screenshot.
  • the above-mentioned information to be embedded may also be information input by the user, such as user name information, password information, and the like.
  • the information to be embedded can be converted into binary data in order to embed the information in the picture more covertly.
  • the value of the DC coefficient is usually tens of times larger than the AC coefficient, or even hundreds of times. Therefore, embedding information into the DC coefficient has better invisibility. Because images embedded with information are easily shared by the client application in the process of signal processing such as lossy compression, low-pass filtering, sub-sampling, interpolation, etc. In these processes, the protection of the DC coefficient is compared to the AC coefficient The protection is better, therefore, the information embedded in the DC coefficient is more robust in the signal processing process.
  • the above embedding process may be a process of modifying the DC coefficient to embed the information to be embedded, that is, a process of replacing the value of the corresponding value bit of the DC coefficient with the information to be embedded.
  • the value of the least significant bit of the DC coefficient is replaced with the information to be embedded.
  • the least significant bit refers to the 0th bit (that is, the least significant bit) in a binary number.
  • the value of the random bit of the DC coefficient is replaced with the information to be embedded.
  • the second transform matrix is used to convert the frequency domain coefficient matrix of the embedded information into the spatial data matrix of the embedded information.
  • the above-mentioned execution subject first performs inverse quantization processing on the frequency domain coefficient matrix; then, performs transform domain processing on the image to convert the image from the frequency domain to the spatial domain.
  • the above step 204 is a step corresponding to the above step 202.
  • the corresponding relationship between the reverse quantization and the quantization uses the same quantization table, and the bit rate is increased through the mapping relationship, that is, the data in the original frequency domain coefficient matrix is multiplied by the quantization coefficient at the corresponding position in the quantization table.
  • the existing and future developed transform domain technology to convert the image from the frequency domain to the spatial domain can be used to fulfill.
  • using a second transformation matrix to convert the frequency-domain coefficient matrix of the embedded information into the spatial-domain data matrix of the embedded information may include: using the inverse transform matrix of the discrete Fourier transform to convert The frequency domain coefficient matrix of the embedded information is converted into the spatial domain data matrix of the embedded information.
  • the above-mentioned inverse transform matrix using discrete Fourier transform corresponds to the discrete Fourier transform matrix used in step 202 above.
  • the image is basically losslessly converted from the frequency domain to the spatial domain, which can improve the fidelity of the image and improve embedding. Concealment of information.
  • using a second transformation matrix to convert the frequency-domain coefficient matrix of the embedded information into the spatial-domain data matrix of the embedded information may include: using an inverse discrete cosine transform matrix to convert the frequency of the embedded information
  • the domain coefficient matrix is converted into a spatial data matrix with embedded information.
  • the inverse discrete cosine transform matrix used above corresponds to the discrete cosine transform matrix used in step 202 above.
  • the transformation using the inverse discrete cosine transform matrix uses a semi-periodic basis function, while the inverse discrete Fourier transform uses Is the basis function of the entire period. Since most pixel changes in the image are gradual, the use of the inverse discrete cosine transform matrix can better express the image, further improving the image fidelity and the concealment of embedded information.
  • Step 205 Based on the spatial data matrix of the embedded information, generate a picture of the embedded information.
  • the spatial data matrix with embedded information is a matrix obtained by storing pixel data of each pixel according to the correspondence between each pixel and the coordinate position. Based on the correspondence between the image pixel data and the image, a picture with embedded information is generated.
  • FIG. 3 is a schematic diagram of an application scenario of the method for embedding information in a picture according to this embodiment.
  • the user opens the login interface of an app program on the mobile phone, and there are three options of "mobile phone registration”, “WeChat login” and “QQ login”. The user clicks "WeChat login”, and the app pops up the message "I know the network error”.
  • the electronic device converts the screenshot from the spatial domain to the frequency domain, performs rounding, quantization, and rounding operations on the frequency domain coefficient matrix, and inserts the information to be embedded 302 "Huawei P9, CPU1.26G available / 3G , The network speed is 1.7M / s, and the memory 64G is embedded in the DC coefficient of the frequency domain coefficient matrix. Finally, the image is converted from the frequency domain to the spatial domain, and a picture 301 with embedded information is generated.
  • the picture 301 contains the relevant information 302 of the mobile phone at the time of the screenshot.
  • the user sends the screenshot to the relevant technician.
  • the technician can obtain the information 302 of the user ’s mobile phone at the time of the screenshot.
  • “Huawei P9, CPU1 .26G available / 3G, network speed 1.7M / s, memory 64G” The technician can easily determine that the user's network status is good, which can help the technician accurately locate the app problem.
  • the method provided by the above embodiment of the present application performs the rounding operation of retaining the integer part of the source data, removing the fractional part of the source data, and embedding the information to be embedded into the DC coefficients of the frequency domain coefficient matrix on the frequency domain coefficient matrix of the image , To achieve the effectiveness and stability of embedding information in the picture.
  • FIG. 4 shows a flow 400 of yet another embodiment of a method of embedding information in a picture.
  • the process 400 of the method for embedding information in a picture includes the following steps:
  • step 401 the first transformation matrix is used to convert the spatial data matrix of the screenshot into the original frequency domain coefficient matrix.
  • step 402 the original frequency domain coefficient matrix is rounded once, wherein the rounding preserves the integer part of the source data and removes the fractional part of the source data.
  • Step 403 the quantization table is used to quantize the original frequency domain coefficient matrix after rounding.
  • step 404 the quantized original frequency domain coefficient matrix is rounded twice, wherein the rounded part retains the integer part of the source data and removes the fractional part of the source data.
  • step 401 is the same as step 201 in the previous embodiment.
  • step 201 also applies to step 401.
  • steps 402, 403, and 404 are the same as step 202 in the previous embodiment.
  • step 202 in this article is also applicable to steps 402, 403, and 404, and is not repeated here.
  • Step 405 Embed the information to be embedded into the DC coefficient of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information.
  • embedding the information to be embedded into the DC coefficients of the intermediate frequency domain coefficient matrix may be a process of rewriting the DC coefficients according to certain rules, and may be implemented using existing and future information embedding technologies.
  • the information can be embedded according to the following steps:
  • the first step it is determined whether the remainder after the DC coefficient is modulo 2 is the same as the information bit to be embedded. For example, the DC coefficient is 32, and the information bit to be embedded is 0, then the remainder after 32-to-2 modulo is 0, which is the same as the information bit 0 to be embedded. For example, the DC coefficient is -31 and the remainder after modulo 2 is 1, and the information bit to be embedded is 0, the two are different.
  • the second step if the two are the same, do not reprogram the DC coefficient. For example, if the DC coefficient is 32 and the information bit to be embedded is 0, the remainder after 32-to-2 modulo is 0, which is the same as the information bit 0 to be embedded, and the information 0 is embedded into the DC coefficient without rewriting the DC coefficient .
  • the DC coefficient is rewritten.
  • the writing rule is to keep the sign bit of the DC coefficient unchanged, and subtract 1 from the value of the value of the DC coefficient.
  • the DC coefficient is -31
  • the information bit to be embedded is 0, the DC coefficient is -31 and the remainder after modulo 2 is 1, the information bit to be embedded is 0, the two are different, and the DC coefficient is rewritten.
  • the newly generated DC coefficient is -30.
  • the above process completes the process of embedding information 0 into the DC coefficient -31.
  • the sign bit of the above reserved DC coefficient remains unchanged, and the value of the value of the DC coefficient is subtracted by 1 to ensure that when the DC coefficient is converted to the spatial domain, the obtained image pixel data is within the threshold range to avoid data overflow errors and ensure Stability of embedded information.
  • information embedding may be performed according to the following steps:
  • the DC coefficient is converted into binary data expressed in the original code, and it is determined whether the lowest bit of the DC coefficient is the same as the information bit to be embedded, where the lowest bit refers to the 0th bit of the binary data.
  • the second step if the same, do not reprogram the DC coefficient.
  • the DC coefficient is re-programmed.
  • the writing rule is to keep the sign bit of the DC coefficient unchanged, and subtract 1 from the value of the DC coefficient.
  • the DC coefficient is -8, and the information bit to be embedded is 1, then -8 is converted to binary data 10001000 expressed in the original code. The lowest bit of the binary data is 0, which is different from the information bit 1 to be embedded.
  • DC coefficient Retain the sign bit of 10001000: 1, subtract 1 from the value of 10001000: 10001000, the value bit becomes: 0000111, the new DC coefficient generated is 10000111, that is, the DC coefficient changes from -8 to-after embedding information 1. 7.
  • the sign bit of the above reserved DC coefficient remains unchanged, and the value of the value of the DC coefficient is subtracted by 1 to ensure that when the DC coefficient is converted to the spatial domain, the obtained image pixel data is within the threshold range to avoid data overflow errors and ensure Stability of embedded information.
  • Step 406 Using the second transformation matrix, convert the frequency-domain coefficient matrix of the embedded information into the spatial-domain data matrix of the embedded information.
  • the above-mentioned execution subject first performs inverse vectorization processing on the frequency domain coefficient matrix; then, performs transform domain processing on the image to convert the image from the frequency domain to the spatial domain.
  • the above step 406 is a step corresponding to the above steps 401 and 403.
  • the existing and future developed transform domain technology to convert the image from the frequency domain to the spatial domain can be used to fulfill.
  • the inverse transform matrix of the discrete Fourier transform can be used, or the inverse discrete cosine transform matrix can also be used to transform the image from the frequency domain to the spatial domain.
  • Step 407 Based on the spatial data matrix of the embedded information, generate a picture of the embedded information.
  • the spatial data matrix with embedded information is a matrix obtained by storing pixel data of each pixel according to the correspondence between each pixel and the coordinate position. Based on the correspondence between the image pixel data and the image, a picture with embedded information is generated.
  • the process 400 of the method for embedding information in a picture in this embodiment refines the DC coefficient of embedding the information to be embedded into the coefficient matrix of the intermediate frequency domain In the step of obtaining the frequency domain coefficient matrix with embedded information.
  • the solution described in this embodiment can ensure that the information embedded in the picture is not lost, thereby enriching the data dimension of the picture, and ensuring the validity and stability of the embedded information.
  • the present application provides an embodiment of a device for embedding information in a picture, which corresponds to the method embodiment shown in FIG. 2,
  • the device can be specifically applied to various electronic devices.
  • the apparatus 500 for embedding information in a picture of this embodiment includes: a coefficient matrix conversion unit 501, a coefficient matrix optimization unit 502, a matrix information embedding unit 503, a data matrix conversion unit 504, and an information picture generating unit 505.
  • the coefficient matrix conversion unit 501 is configured to convert the screenshot spatial domain data matrix to the original frequency domain coefficient matrix; the coefficient matrix optimization unit 502 is configured to perform primary rounding, quantization, and secondary rounding on the original frequency domain coefficient matrix To obtain an intermediate frequency domain coefficient matrix, where the first rounding and the second rounding respectively retain the integer part of the source data and remove the fractional part of the source data; the matrix information embedding unit 503 is configured to embed the information to be embedded into the intermediate frequency domain Among the DC coefficients of the coefficient matrix, the frequency domain coefficient matrix of the embedded information is obtained; the data matrix conversion unit 504 is configured to convert the frequency domain coefficient matrix of the embedded information into the spatial domain data matrix of the embedded information; and the information picture generating unit 505 is configured to generate a picture of the embedded information based on the spatial data matrix of the embedded information.
  • the coefficient matrix conversion unit 501 of the device 500 for embedding information in the picture can convert the picture from the spatial domain to the frequency domain to obtain the original frequency domain coefficient matrix.
  • some masking characteristics of the human perception system can be more easily integrated into the process of information embedding. Decomposing and processing the frequency coefficients of the image in the frequency domain can effectively embed the information to be embedded into the picture .
  • the coefficient matrix optimization unit 502 can perform rounding, quantization, and second rounding on the original frequency domain coefficient matrix to obtain an intermediate frequency domain coefficient matrix, where the first rounding and the second rounding respectively retain the source
  • the integer part of the data excluding the decimal part of the source data.
  • the quantization process is actually an optimization process for frequency coefficients. It takes advantage of the insensitivity of the human eye to high-frequency parts to achieve a substantial simplification of data and improve computational efficiency.
  • the device 500 to embed information in the picture may store the information to be embedded in advance, and the information to be embedded may be some logo information, such as copyright declaration information, etc.
  • the information to be embedded may also be the device 500 to embed
  • the information generated by the set rule for example, the information to be embedded may be information reflecting the operating state of the device on which the device 500 is installed when the screenshot is taken, so as to record the operating state of the device.
  • the matrix information embedding unit 503 embeds the information to be embedded into the DC coefficient.
  • the above embedding process may be a process of modifying the DC coefficient to embed the information to be embedded, that is, replacing the corresponding value bits of the DC coefficient with the information to be embedded Numerical process. Make the information to be embedded hidden and stable embedded in the source picture data.
  • the above-mentioned data matrix conversion unit 504 may convert the picture from the frequency domain to the spatial domain.
  • the information picture generating unit 505 may generate a picture with embedded information according to the image pixel data in the spatial domain data matrix of the embedded information.
  • the units recorded in the device 500 may correspond to the various steps in the method described with reference to FIGS. 2 to 4. Therefore, the operations and features described above for the method are also applicable to the device 500 and the units contained therein, which will not be repeated here.
  • FIG. 6 shows a schematic structural diagram of a computer system 600 suitable for implementing the terminal device of the embodiment of the present application.
  • the terminal device shown in FIG. 6 is only an example, and should not bring any limitation to the functions and usage scope of the embodiments of the present application.
  • the computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a random access memory (RAM) 603 from a program stored in a read-only memory (ROM) 602 or from a storage section 608 Instead, perform various appropriate actions and processing.
  • RAM random access memory
  • ROM read-only memory
  • various programs and data necessary for the operation of the system 600 are also stored.
  • the CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604.
  • An input / output (I / O) interface 605 is also connected to the bus 604.
  • the following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc .; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and speakers; ; And a communication section 609 including a network interface card such as a LAN card, a modem, etc.
  • the communication section 609 performs communication processing via a network such as the Internet.
  • the driver 610 is also connected to the I / O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 610 as necessary, so that the computer program read out therefrom is installed into the storage section 608 as needed.
  • the process described above with reference to the flowchart may be implemented as a computer software program.
  • embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, the computer program containing program code for performing the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication section 609, and / or installed from the removable medium 611.
  • CPU central processing unit
  • the above-mentioned functions defined in the method of the present application are executed.
  • the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable removable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal that is propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried.
  • This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. .
  • the program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the foregoing.
  • the computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, the programming languages including object-oriented programming languages such as Java, Smalltalk, C ++, as well as conventional Procedural programming language-such as "C" language or similar programming language.
  • the program code may be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, through an Internet service provider Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider Internet connection for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code that contains one or more logic functions Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may actually be executed in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.
  • each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts can be implemented with dedicated hardware-based systems that perform specified functions or operations Or, it can be realized by a combination of dedicated hardware and computer instructions.
  • the units described in the embodiments of the present application may be implemented in software or hardware.
  • the described unit may also be provided in the processor, for example, it may be described as: a processor includes a coefficient matrix conversion unit 501, a coefficient matrix optimization unit 502, a matrix information embedding unit 503, a data matrix conversion unit 504, and information picture generation Unit 505.
  • a processor includes a coefficient matrix conversion unit 501, a coefficient matrix optimization unit 502, a matrix information embedding unit 503, a data matrix conversion unit 504, and information picture generation Unit 505.
  • the names of these units do not constitute a limitation on the unit itself.
  • the coefficient matrix optimization unit can also be described as "a unit that obtains the coefficient matrix of the intermediate frequency domain.”
  • the present application also provides a computer-readable medium, which may be included in the device described in the foregoing embodiments; or may exist alone without being assembled into the device.
  • the above computer readable medium carries one or more programs.
  • the device is caused to: use the first transformation matrix to convert the spatial domain data matrix of the screenshot into the original frequency domain coefficient matrix ; Perform primary rounding, quantization and secondary rounding on the original frequency domain coefficient matrix to obtain the intermediate frequency domain coefficient matrix, where the primary rounding and secondary rounding respectively retain the integer part of the source data and remove the fractional part of the source data ; Embed the information to be embedded into the DC coefficient of the intermediate frequency domain coefficient matrix to obtain the frequency domain coefficient matrix of the embedded information; use the second transformation matrix to convert the frequency domain coefficient matrix of the embedded information into the spatial domain of the embedded information Data matrix; generate a picture of embedded information based on the spatial data matrix of the embedded information.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Image Processing (AREA)

Abstract

Selon certains modes de réalisation, la présente invention concerne un procédé et un dispositif permettant d'intégrer des informations dans une image. Un mode de réalisation spécifique du procédé comprend les étapes consistant à : utiliser une première matrice de transformation pour convertir une matrice de données de domaine spatial d'une capture d'écran en une matrice de coefficient de domaine fréquentiel d'origine ; effectuer un arrondissement primaire, une quantification, et un arrondissement secondaire sur la matrice de coefficient de domaine fréquentiel d'origine de façon à acquérir une matrice de coefficient de domaine fréquentiel intermédiaire, une partie entière de données de source étant maintenue et une partie décimale des données de source étant supprimée pendant l'arrondissement primaire et l'arrondissement secondaire, respectivement ; intégrer, dans un coefficient de courant continu de la matrice de coefficient de domaine fréquentiel intermédiaire, des informations à intégrer de façon à acquérir une matrice de coefficient de domaine fréquentiel comprenant des informations intégrées ; utiliser une seconde matrice de transformation pour convertir la matrice de coefficient de domaine fréquentiel comprenant les informations intégrées en une matrice de données de domaine spatial comprenant les informations intégrées ; et générer, sur la base de la matrice de données de domaine spatial comprenant les informations intégrées, une image comprenant les informations intégrées. Le mode de réalisation assure que les informations intégrées dans une image sont valides et stables.
PCT/CN2019/103578 2018-11-06 2019-08-30 Procédé et dispositif permettant d'intégrer des informations dans une image WO2020093769A1 (fr)

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