CN117408861A - Beacon lossless construction method for generated image - Google Patents

Beacon lossless construction method for generated image Download PDF

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CN117408861A
CN117408861A CN202311302575.4A CN202311302575A CN117408861A CN 117408861 A CN117408861 A CN 117408861A CN 202311302575 A CN202311302575 A CN 202311302575A CN 117408861 A CN117408861 A CN 117408861A
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beacon
image
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generated image
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毛志杰
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National University of Defense Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0021—Image watermarking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/001—Texturing; Colouring; Generation of texture or colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0052—Embedding of the watermark in the frequency domain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0202—Image watermarking whereby the quality of watermarked images is measured; Measuring quality or performance of watermarking methods; Balancing between quality and robustness
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0203—Image watermarking whereby the image with embedded watermark is reverted to the original condition before embedding, e.g. lossless, distortion-free or invertible watermarking

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Probability & Statistics with Applications (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses a beacon lossless construction method for a generated image. The beacon lossless construction method for the generated image comprises the following steps: s1, generating an initial image set to obtain; s2, generating an image beacon parameter; s3, generating an image target beacon selection; s4, generating image target beacon position selection; s5, obtaining fault-tolerant parameters of the generated image target beacon; s6, generating an image target beacon embedding; s7, generating a formula image target beacon verification. According to the method, the positions of the generated image target beacons and the embedded images are determined according to the generated image beacon parameters and the generated image target beacon position parameters, fault tolerance parameters are added in the process of embedding the generated image target beacons and the embedded images by analyzing beacons, the generated image beacon loss is calculated by extracting the beacons to verify the generated image target beacons, the effect of standardized evaluation of the generated image-oriented beacon construction method is achieved, and the problem that the hidden information construction evaluation method is not standardized in the prior art is solved.

Description

Beacon lossless construction method for generated image
Technical Field
The invention relates to the technical field of computer vision, in particular to a beacon lossless construction method for a generated image.
Background
The image generation model comprises two main types of unconditional generation and conditional generation. Unconditional generation is mainly performed by a variable auto-encoder (VAE), and the size of an image generated through the dimension up and down operations is not changed. Conditional generation mainly consists in generating a countermeasure network (Generative Adversarial Networks, GAN), generating realistic images by training a generator network and a discriminator network. Along with the increasing demands of people on the aspects of image authentication, security, copyright protection, privacy protection and the like, the image information hiding technology is rapidly developed.
The image information hiding technology is mainly realized by two modes of embedding beacons and digital watermarks. The beacon construction refers to embedding a beacon with specific information at a certain position in media on the premise of not affecting the quality of the media such as images, audio and video, and the beacon construction is applied to authenticating and verifying images and can also be used for anonymizing and authenticating pictures. Digital watermarking is a technique of embedding hidden information into an image, and can be used for image authentication, copyright protection and tracking.
For example, publication No.: the invention patent of CN114777757A discloses a beacon map construction method, a device, equipment and a storage medium, comprising: acquiring a beacon position measured by measuring equipment at an i-1 site; according to pose change information of station changing, a first pose constraint relation of the measurement equipment at an ith station relative to an i-1 th station is obtained; acquiring a second pose constraint relation of the ith station relative to the beacon according to the beacon pose acquired by the ith station and combining the determined positioned beacon; determining an error equation of an ith site according to the first pose constraint relation and the second pose constraint relation; and optimizing the error equation to determine the position of the ith station, and determining a beacon map according to the determined station position.
For example, publication No.: the invention patent of CN103455966A discloses a digital watermark embedding device, a digital watermark embedding method and a digital watermark detecting device, wherein the digital watermark embedding device comprises: an interface unit for acquiring video data and digital watermark information; and a processing unit for embedding digital watermark information into the video data. The processing unit is configured such that an area of a watermark pattern formed by a plurality of pixels having a specified value and superimposed on each image included in video data changes in a periodic manner with time in accordance with a value of a symbol included in the digital watermark information, and corrects the value of each pixel included in each image in the video data and a region where the watermark pattern corresponding to the image overlap each other using the specified value.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the inventor of the application finds that at least the following technical problems exist in the above technology:
in the prior art, the beacon construction optimizes and obtains the next beacon by using the existing result and error equation, the digital watermark construction realizes the acquisition of video data and digital watermark and the embedding of watermark information by using an interface unit and a processing unit, and the problem that a hidden information construction evaluation method is not standard exists.
Disclosure of Invention
The beacon lossless construction method for the generated image solves the problem that the hidden information construction evaluation method is not standard in the prior art, and achieves standardized evaluation of the beacon lossless construction method for the generated image.
The embodiment of the application provides a beacon lossless construction method for a generated image, which comprises the following steps: s1, generating an initial image set to obtain: selecting an image generation model to obtain a generation type initial image according to the image generation principle, and constructing a generation type initial image set; s2, acquiring generated image beacon parameters: analyzing all the generated initial images in the generated initial image set to obtain image characteristic parameters of the generated images, and determining generated image beacon parameters according to the image characteristic parameters of the generated images; s3, generating image target beacon selection: analyzing the relation of the generated image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon parameters, substituting the generated image target beacon parameters into a generated image target beacon formula to select a generated image target beacon; s4, generating image target beacon position selection: analyzing the generated initial image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon position parameters, substituting the generated image target beacon position parameters into a generated image target beacon position index formula to calculate a generated image target beacon position index, and obtaining a generated image target beacon position; s5, obtaining fault-tolerant parameters of the generated image target beacon: analyzing the process of embedding the generated image target beacon into the generated initial image, and acquiring fault-tolerant parameters of the generated image target beacon; s6, embedding a generated image target beacon: analyzing the generated image target beacon, adding fault-tolerant parameters of the generated image target beacon, and embedding the generated image target beacon into the generated image target beacon position in the generated initial image according to the beacon embedding principle to obtain a generated image target beacon image; s7, generating image target beacon verification: and extracting a beacon from the generated type target beacon image by using a beacon extraction method to obtain a generated type image reference beacon, analyzing the correlation between the generated type image reference beacon and the generated type image target, analyzing the correlation between the generated type target beacon image and the generated type initial image to obtain a beacon loss parameter, substituting the beacon loss parameter into the generated type image beacon loss formula to calculate the generated type image beacon loss, and verifying the loss degree of the generated type image beacon embedding.
Further, the set of generated initial images in S1 is a set of generated initial images, and the generated initial images are high-quality images.
Further, the image beacon parameters generated in S2 are a gray level set, a color channel set, an image resolution set, an image texture and an image frequency, which are specifically as follows: the color channel set is a beacon combination of red channel, green channel and blue channel values at a selected area, and the specific obtaining steps are as follows: according to the red channel, green channel and blue channel values, a color channel set is obtained through a formula, and the specific calculation formula is as followsWherein Color (Color channel), color (red channel), color (green channel) and Color (blue channel) are Color channel, red channel, green channel and blue channel values, respectively, +.>To round the symbol alpha 1 、β 1 And gamma 1 Coefficients of red channel, green channel and blue channel values, respectively, are calculated asAnde is a natural constant; the set of image resolutions is a numerical value of the image resolution.
Further, the image target beacon parameters generated in S3 are a color channel set, an image resolution set and a gray frequency set, which are specifically as follows: the gray frequency set is a set of frequency of each gray in the generated initial image and is obtained by a gray histogram; the specific acquisition steps of the generated image target beacon are as follows: according to the Color channel set, the image Resolution set and the Gray frequency set, a generated image target beacon is obtained through a formula, wherein the specific calculation formula is XB (target beacon) = { Color (Color channel) } U { Resolution (image Resolution set) } U { Gray (Gray frequency set) }, and XB (target beacon), color (Color channel), resolution (image Resolution set) and Gray (Gray frequency set) are respectively the generated image target beacon, the Color channel set of the generated initial image, the image Resolution set of the generated initial image and the Gray frequency set of the generated initial image.
Further, the generated image target beacon position parameter in S4 is an image texture and an image frequency, and the generated image target beacon position index is calculated from the generated image target beacon position parameter, which specifically includes the following steps: the image texture is commonly measured by using entropy and angular second moment, and the specific obtaining steps are as follows: the image texture W is obtained by a formula according to the entropy E (x, y) and the angular second moment ASM (x, y) Texture and method for producing the same (x, y) the specific calculation formula ise is a natural constant;
the specific obtaining step of the generated image target beacon position index comprises the following steps of: according to image texture W Texture and method for producing the same (x, y) obtaining a generated image target beacon position index by a formula, wherein the specific calculation formula is as followsWherein L (x, y) and P Frequency of (x, y) are the generated image target beacon location index and image frequency at (x, y) in the image, respectively; the generated image target beacon position is the maximum generated image target beacon position index region of the reference region in the generated initial image, and the generated image reference region is the top left corner vertex, the bottom left corner vertex, the top right corner vertex, the bottom right corner vertex and the centroid of the generated initial image.
Further, the generated type image target beacon fault tolerance parameter in S5 is a generated type image transformation fault tolerance parameter and a generated type image target beacon coding check code, which specifically includes the following steps: the generated image transformation fault-tolerant parameter is a target frequency threshold of the generated initial image; the generated image target beacon coding check code is a forward error correction code and a cyclic redundancy code.
Further, the specific process of obtaining the generated target beacon image in S6 is as follows: s61, frequency domain image acquisition: converting the generated initial image from a space domain to a frequency domain through Fourier transformation, and setting a generated image transformation fault tolerance parameter to obtain a frequency domain image; s62, acquiring frequency domain data of a target beacon: carrying out Huffman coding on the generated image target beacon, and adding a generated image target beacon coding check code to obtain target beacon frequency domain data in a numerical form; s63, embedding frequency domain data of the target beacon: embedding the target beacon frequency domain data into a generated image target beacon position of the frequency domain image to obtain a target beacon frequency domain image; s64, generating target beacon image acquisition: and converting the target beacon frequency domain image from a frequency domain to a space domain through inverse Fourier transform to obtain a generated target beacon image.
Further, in S7, the comparison and verification with the generated image target beacon specifically includes the following steps: s71, generating an image reference beacon extraction: converting the generated target beacon image from a space domain to a frequency domain through Fourier transformation, extracting target beacon frequency domain data, and decoding the target beacon frequency domain data through Huffman decoding to obtain a generated image reference beacon; s72, calculating an image loss: analyzing the relation between the generated target beacon image and the generated initial image to obtain an image loss parameter, and calculating the image loss of the generated target beacon image and the generated initial image through an image loss formula; s73, calculating a beacon loss: analyzing the relation between the generated image reference beacon and the generated image target beacon to obtain beacon loss parameters, and calculating the beacon loss of the generated image reference beacon and the generated image target beacon through a beacon loss formula; s74, calculating a generated image beacon loss: and obtaining the generated image beacon loss from the image loss and the beacon loss.
Further, the image loss and the beacon loss are specifically calculated as follows: the image loss parameters are typical evaluation indexes of the similarity of two images, namely structural similarity, perceptual hash and peak signal-to-noise ratio; the specific image loss obtaining step comprises the following steps: according to the image loss parameters, obtaining the image loss through a formula, wherein the specific calculation formula is as followsWherein Loss is Image processing apparatus 、SSIM Image processing apparatus 、pHash Image processing apparatus And PNSR (PNSR) Image processing apparatus The method comprises the steps of respectively generating an image loss, structural similarity, perceptual hash and peak signal-to-noise ratio of a generated target beacon image and a generated initial image, wherein e is a natural constant; the beacon loss parameters are typical evaluation indexes of two groups of data correlations, namely linear correlations, variances, standard deviations and kurtosis; the specific acquisition steps of the beacon loss are as follows: the beacon loss is obtained by a formula based on the linear correlation, variance, standard deviation and kurtosis, the specific calculation formula is +>Wherein Loss is Beacon beacon 、R1 Beacon beacon 、R2 Beacon beacon 、R3 Beacon beacon And R4 Beacon beacon Respectively generating image reference signalsBeacon loss, beacon linear correlation, beacon variance correlation, beacon standard deviation correlation and beacon kurtosis correlation of the beacon and the generated image target beacon; the specific beacon variance correlation obtaining steps of the generated image reference beacon and the generated image target beacon are as follows: obtaining a beacon variance correlation R2 of the generated image reference beacon and the generated image target beacon by a formula according to the variance of the generated image reference beacon and the variance of the generated image target beacon Beacon beacon The specific calculation formula is->Wherein Sd Target beacon And Sd Reference beacon The variance of the generated image target beacon and the variance of the generated image reference beacon are respectively; the specific beacon standard deviation correlation obtaining step of the generated image reference beacon and the generated image target beacon comprises the following steps: obtaining a beacon standard deviation correlation R3 of the generated image reference beacon and the generated image target beacon through a formula according to the standard deviation of the image reference beacon and the standard deviation of the generated image target beacon Beacon beacon The specific calculation formula is R3 Beacon beacon The specific calculation formula isWherein Cov is Target beacon And Cov Reference beacon The standard deviation of the generated image target beacon and the standard deviation of the generated image reference beacon are respectively; the specific obtaining step of the beacon kurtosis correlation of the generated image reference beacon and the generated image target beacon comprises the following steps: obtaining a beacon kurtosis correlation R4 of the generated image reference beacon and the generated image target beacon by a formula according to the kurtosis of the generated image reference beacon and the generated image target beacon Beacon beacon The specific calculation formula is->Wherein K is Target beacon And K Reference beacon Kurtosis of a generated image target beacon and a generated image reference beacon, respectivelyKurtosis of (c).
Further, the specific obtaining step of the generated image beacon loss comprises the following steps: obtaining the generated image beacon loss according to the image loss and the variance loss by a formula, wherein the specific calculation formula is as followsWherein Loss is Overall (L) Loss of beacons for the generated images.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the method comprises the steps of obtaining a generated initial image set, obtaining generated image beacon parameters, selecting a generated image target beacon position and obtaining generated image target beacon fault tolerance parameters, so that the generated image target beacon is embedded in the generated image target beacon position in the generated initial image to obtain a generated target beacon image, verifying the embedded loss degree of the generated image beacon, further realizing standardized evaluation of a beacon lossless construction method for the generated image, and effectively solving the problem that a hidden information construction evaluation method is not standardized in the prior art.
2. The generated initial image is converted from a space domain to a frequency domain through Fourier transformation to obtain a frequency domain image, and Huffman coding of a generated image target beacon is carried out to obtain target beacon frequency domain data in a numerical form, so that the target beacon frequency domain data is embedded into a generated image target beacon position of the frequency domain image to obtain a target beacon frequency domain image, and further accurate and rapid conversion from the frequency domain to the space domain of the target beacon frequency domain image is realized.
3. And extracting the generated image reference beacon from the generated target beacon image through Fourier transformation and Huffman decoding, so that the generated image beacon loss is obtained by calculating the image loss and the beacon loss in the beacon embedding of the generated image through an image loss formula and a beacon loss formula, and further, the numerical measurement of the beacon embedding loss degree of the generated image is realized.
Drawings
Fig. 1 is a flowchart of a beacon lossless construction method for a generated image according to an embodiment of the present application;
fig. 2 is a flowchart of a generated target beacon image acquisition provided in an embodiment of the present application;
fig. 3 is a flowchart of verification of a generated image target beacon according to an embodiment of the present application.
Detailed Description
The beacon lossless construction method for the generated image solves the problem that the hidden information construction evaluation method is not standard in the prior art, the generated image beacon parameters and the generated image target beacon position parameters are used for determining the positions of the generated image target beacon and the embedded image, the fault tolerance parameters are added in the process of analyzing the beacon embedding, then the beacon is embedded, the generated image beacon loss is extracted, the generated image target beacon is verified, and the standardized evaluation of the beacon lossless construction method for the generated image is realized.
The technical scheme in the embodiment of the application aims to solve the problem that the hidden information construction evaluation method in the prior art is not standard, and the overall thought is as follows:
the position of the generated image target beacon and the embedded image is determined through the generated image beacon parameters and the generated image target beacon position parameters, fault tolerance parameters are added in the process of analyzing the beacon embedding, then the generated image beacon loss is calculated by extracting the beacon to verify the generated image target beacon, and the effect of the normalized evaluation of the beacon lossless construction method facing the generated image is achieved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flowchart of a beacon lossless construction method for a generated image according to an embodiment of the present application is provided, and the method includes the following steps: s1, generating an initial image set to obtain: selecting an image generation model to obtain a generation type initial image according to the image generation principle, and constructing a generation type initial image set; s2, acquiring generated image beacon parameters: analyzing all the generated initial images in the generated initial image set to obtain image characteristic parameters of the generated images, and determining generated image beacon parameters according to the image characteristic parameters of the generated images; s3, generating image target beacon selection: analyzing the relation of the generated image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon parameters, substituting the generated image target beacon parameters into a generated image target beacon formula to select a generated image target beacon; s4, generating image target beacon position selection: analyzing the generated initial image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon position parameters, substituting the generated image target beacon position parameters into a generated image target beacon position index formula to calculate a generated image target beacon position index, and obtaining a generated image target beacon position; s5, obtaining fault-tolerant parameters of the generated image target beacon: analyzing the process of embedding the generated image target beacon into the generated initial image, and acquiring fault-tolerant parameters of the generated image target beacon; s6, embedding a generated image target beacon: analyzing the generated image target beacon, adding fault-tolerant parameters of the generated image target beacon, and embedding the generated image target beacon into the generated image target beacon position in the generated initial image according to the beacon embedding principle to obtain a generated image target beacon image; s7, generating image target beacon verification: and extracting a beacon from the generated type target beacon image by using a beacon extraction method to obtain a generated type image reference beacon, analyzing the correlation between the generated type image reference beacon and the generated type image target, analyzing the correlation between the generated type target beacon image and the generated type initial image to obtain a beacon loss parameter, substituting the beacon loss parameter into the generated type image beacon loss formula to calculate the generated type image beacon loss, and verifying the loss degree of the generated type image beacon embedding.
In the embodiment, the standardization of the evaluation of the beacon lossless construction method for the generated image is realized through the steps of generating an initial image set acquisition, generating an image beacon parameter acquisition, generating an image target beacon selection, generating an image target beacon position selection, generating an image target beacon fault tolerance parameter acquisition, generating an image target beacon embedding and generating an image target beacon verification.
Further, in S1, the set of generated expression initial images is a set of generated expression initial images, and the generated expression initial images are high-quality images.
In the embodiment, the high-quality generation type initial image is obtained by selecting the image generation model, and the generation type initial image set is constructed, so that the universality of the beacon lossless construction method for the generation type image is enhanced.
Further, the image beacon parameters generated in S2 are a gray level set, a color channel set, an image resolution set, an image texture and an image frequency, which are specifically as follows: the color channel set is a beacon combination of red channel, green channel and blue channel values at a selected area, and the specific obtaining steps are as follows: according to the red channel, green channel and blue channel values, a color channel set is obtained through a formula, and the specific calculation formula is as follows Wherein Color (Color channel), color (red channel), color (green channel) and Color (blue channel) are Color channel, red channel, green channel and blue channel values, respectively, +.>To round the symbol alpha 1 、β 1 And gamma 1 Coefficients of red channel, green channel and blue channel values, respectively, are calculated asAnde is a natural constant; the image resolution set is a numerical value of the image resolution.
In this embodiment, the resolution is one of important indexes for measuring the image quality, the high-resolution image has more details and definition, and the image characteristic parameters of the generated image are obtained by analyzing all the generated initial images in the generated initial image set so as to determine the generated image beacon parameters, thereby enhancing the uniqueness of the beacon lossless construction method facing the generated image.
Further, the parameters of the image target beacon generated in S3 are a color channel set, an image resolution set and a gray frequency set, which are specifically as follows: the gray frequency set is a set of frequency of occurrence of each gray in the generated initial image, and is obtained by a gray histogram, wherein gray level in the gray histogram is the horizontal axis, and frequency or number of occurrence is the vertical axis; the specific acquisition steps of the generated image target beacon are as follows: according to the Color channel set, the image Resolution set and the Gray frequency set, a generated image target beacon is obtained through a formula, wherein the specific calculation formula is XB (target beacon) = { Color (Color channel) } U { Resolution (image Resolution set) } U { Gray (Gray frequency set) }, and XB (target beacon), color (Color channel), resolution (image Resolution set) and Gray (Gray frequency set) are respectively the generated image target beacon, the Color channel set of the generated initial image, the image Resolution set of the generated initial image and the Gray frequency set of the generated initial image.
In this embodiment, the generated image target beacon parameters are obtained by analyzing the generated image beacon parameter relation of all the generated initial images in the generated initial image set, and then the generated image target beacon is selected, so that the enhancement of the uniqueness of the generated image-oriented beacon is realized.
Further, in S4, the generated image target beacon position parameter is an image texture and an image frequency, and the generated image target beacon position index is calculated from the generated image target beacon position parameter, which specifically includes the following steps: the image texture is commonly measured by using entropy and angular second moment, and the specific obtaining steps are as follows: the image texture W is obtained by a formula according to the entropy E (x, y) and the angular second moment ASM (x, y) Texture and method for producing the same (x, y) the specific calculation formula ise is a natural constant; the specific obtaining step of the generated image target beacon position index is as follows: according to image texture W Texture and method for producing the same (x, y) obtaining a generated image target beacon position index by a formula of +.>Wherein L (x, y) and P Frequency of (x, y) are the generated image target beacon location index and image frequency at (x, y) in the image, respectively; the generated image target beacon position is the maximum generated image target beacon position index region of the reference region in the generated initial image, and the generated image reference region is the top left corner vertex, the bottom left corner vertex, the top right corner vertex, the bottom right corner vertex and the centroid of the generated initial image.
In this embodiment, the generated image target beacon position is obtained by analyzing the generated initial image beacon parameters of all generated initial images in the generated initial image set and calculating the generated image target beacon position index, so that the enhancement of the generated image target beacon embedding position selection normalization and the reduction of the loss are realized.
Further, the fault-tolerant parameters of the generated image target beacon in S5 are generated image transformation fault-tolerant parameters and generated image target beacon coding check codes, which are specifically as follows: the generated image transformation fault-tolerant parameter is a target frequency threshold of the generated initial image, and noise and false detection can be filtered through the target frequency threshold; the generated image target beacon coding check code is a forward error correction code and a cyclic redundancy code, and the cyclic redundancy code can reduce errors possibly introduced in the transmission or storage process.
In the embodiment, the fault tolerance parameter of the generated image target beacon is obtained through analyzing the process of embedding the generated image target beacon into the generated initial image, so that the reduction of the error rate and the improvement of the robustness of the generated image target beacon embedding process are realized.
Further, as shown in fig. 2, in the flowchart for obtaining the generated target beacon image provided in the embodiment of the present application, the specific process of obtaining the generated target beacon image in S6 is as follows: s61, frequency domain image acquisition: converting the generated initial image from a spatial domain to a frequency domain through Fourier transformation, setting the generated image transformation fault tolerance parameter to obtain a frequency domain image, and enabling the stability of the frequency domain image to be higher; s62, acquiring frequency domain data of a target beacon: carrying out Huffman coding on the generated image target beacon, and adding a generated image target beacon coding check code to obtain target beacon frequency domain data in a numerical form; s63, embedding frequency domain data of the target beacon: embedding the frequency domain data of the target beacon into the generated image target beacon position of the frequency domain image to obtain the frequency domain image of the target beacon; s64, generating target beacon image acquisition: and converting the frequency domain image of the target beacon from the frequency domain to the space domain through inverse Fourier transform to obtain a generated target beacon image.
In the embodiment, the generated image target beacon image is obtained by analyzing the generated image target beacon, adding fault tolerance parameters of the generated image target beacon and embedding the generated image target beacon, so that the reliability, stability and uniqueness of embedding the generated image target beacon are improved.
Further, as shown in fig. 3, in the verification flowchart of the generated image target beacon provided in the embodiment of the present application, the comparison verification with the generated image target beacon in S7 includes the following specific steps: s71, generating an image reference beacon extraction: converting the generated target beacon image from a space domain to a frequency domain through Fourier transformation, extracting target beacon frequency domain data, and decoding the target beacon frequency domain data through Huffman decoding to obtain a generated image reference beacon; s72, calculating an image loss: analyzing the relation between the generated type target beacon image and the generated type initial image to obtain an image loss parameter, and calculating the image loss of the generated type target beacon image and the generated type initial image through an image loss formula, wherein the larger the image loss value is, the easier the generated type image beacon is detected; s73, calculating a beacon loss: analyzing the relation between the generated image reference beacon and the generated image target beacon to obtain a beacon loss parameter, and calculating the beacon losses of the generated image reference beacon and the generated image target beacon through a beacon loss formula, wherein the larger the beacon loss value is, the worse the stability of the generated image target beacon is; s74, calculating a generated image beacon loss: the resulting image beacon penalty is derived from the image penalty and the beacon penalty.
In the present embodiment, the degree of loss of the generated image beacon embedding is verified by extracting a beacon from the generated image target beacon image using the beacon extraction method, analyzing the correlation of the generated image reference beacon and the generated image target, analyzing the correlation calculation of the generated image target beacon image and the generated initial image.
Further, the image loss and the beacon loss are specifically calculated as follows: the image loss parameters are typical evaluation indexes of the similarity of two images, namely structural similarity, perceptual hash and peak signal-to-noise ratio; the specific acquisition steps of the image loss are as follows: according to the image loss parameters, obtaining the image loss through a formula, wherein the specific calculation formula is as followsWherein Loss is Image processing apparatus 、SSIM Image processing apparatus 、pHash Image processing apparatus And PNSR (PNSR) Image processing apparatus The method comprises the steps of respectively generating an image loss, structural similarity, perceptual hash and peak signal-to-noise ratio of a generated target beacon image and a generated initial image, wherein e is a natural constant; the beacon loss parameters are typical evaluation indexes of two groups of data correlation, and are linear correlation, variance, standard deviation and kurtosis respectively; the specific acquisition steps of the beacon loss are as follows: the beacon loss is obtained by a formula based on the linear correlation, variance, standard deviation and kurtosis, the specific calculation formula is + >Wherein Loss is Beacon beacon 、R1 Beacon beacon 、R2 Beacon beacon 、R3 Beacon beacon And R4 Beacon beacon Beacon loss, beacon linear correlation, beacon variance correlation, beacon standard deviation correlation and beacon kurtosis correlation of the generated image reference beacon and the generated image target beacon respectively; the specific beacon variance correlation obtaining steps of the generated image reference beacon and the generated image target beacon are as follows: according to living thingsVariance of the formed image reference beacon and variance of the generated image target beacon, and beacon variance correlation R2 of the generated image reference beacon and the generated image target beacon is obtained through a formula Beacon beacon The specific calculation formula is->Wherein Sd Target beacon And Sd Reference beacon The variance of the generated image target beacon and the variance of the generated image reference beacon are respectively; the specific beacon standard deviation correlation obtaining steps of the generated image reference beacon and the generated image target beacon are as follows: obtaining a beacon standard deviation correlation R3 of the generated image reference beacon and the generated image target beacon through a formula according to the standard deviation of the image reference beacon and the standard deviation of the generated image target beacon Beacon beacon The specific calculation formula is->Wherein Cov is Target beacon And Cov Reference beacon The standard deviation of the generated image target beacon and the standard deviation of the generated image reference beacon are respectively; the specific obtaining steps of the beacon kurtosis correlation of the generated image reference beacon and the generated image target beacon are as follows: obtaining a beacon kurtosis correlation R4 of the generated image reference beacon and the generated image target beacon by a formula according to the kurtosis of the generated image reference beacon and the generated image target beacon Beacon beacon The specific calculation formula is->Wherein K is Target beacon And K Reference beacon Kurtosis of the generated image target beacon and kurtosis of the generated image reference beacon, respectively.
In the embodiment, the loss of the beacon lossless construction method of the generated image is reduced by obtaining the image loss and the beacon loss through six indexes of structural similarity, perceptual hash, peak signal-to-noise ratio, linear correlation, variance, standard deviation and kurtosis.
Further, the specific obtaining step of the generated image beacon loss comprises the following steps: obtaining the generated image beacon loss according to the image loss and the variance loss by a formula, wherein the specific calculation formula is as followsWherein Loss is Overall (L) Loss of beacons for the generated images.
In the present embodiment, the generation-type image beacon loss is calculated by the image loss and the beacon loss, and the numerical measurement of the beacon embedding loss degree of the generation-type image is realized
The technical scheme in the embodiment of the application at least has the following technical effects or advantages: relative to publication No.: the embodiment of the application obtains the generated target beacon image by embedding the generated image target beacon into the generated initial image through the generated initial image set acquisition, the generated image beacon parameter acquisition, the generated image target beacon selection, the generated image target beacon position selection and the generated image target beacon fault tolerance parameter; relative to publication No.: according to the digital watermark embedding device, the digital watermark embedding method and the digital watermark detection device disclosed by the CN103455966A, the frequency domain image acquisition and the target beacon frequency domain data acquisition are adopted in the embodiment of the application, so that the target beacon frequency domain data are embedded into the generated image target beacon position of the frequency domain image to obtain the target beacon frequency domain image, and the target beacon frequency domain image is converted from the frequency domain to the space domain through inverse Fourier transform to obtain the generated target beacon image.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. The beacon lossless construction method for the generated image is characterized by comprising the following steps of:
s1, generating an initial image set to obtain: selecting an image generation model to obtain a generation type initial image according to the image generation principle, and constructing a generation type initial image set;
s2, acquiring generated image beacon parameters: analyzing all the generated initial images in the generated initial image set to obtain image characteristic parameters of the generated images, and determining generated image beacon parameters according to the image characteristic parameters of the generated images;
s3, generating image target beacon selection: analyzing the relation of the generated image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon parameters, substituting the generated image target beacon parameters into a generated image target beacon formula to select a generated image target beacon;
S4, generating image target beacon position selection: analyzing the generated initial image beacon parameters of all the generated initial images in the generated initial image set to obtain generated image target beacon position parameters, substituting the generated image target beacon position parameters into a generated image target beacon position index formula to calculate a generated image target beacon position index, and obtaining a generated image target beacon position;
s5, obtaining fault-tolerant parameters of the generated image target beacon: analyzing the process of embedding the generated image target beacon into the generated initial image, and acquiring fault-tolerant parameters of the generated image target beacon;
s6, embedding a generated image target beacon: analyzing the generated image target beacon, adding fault-tolerant parameters of the generated image target beacon, and embedding the generated image target beacon into the generated image target beacon position in the generated initial image according to the beacon embedding principle to obtain a generated image target beacon image;
s7, generating image target beacon verification: and extracting a beacon from the generated type target beacon image by using a beacon extraction method to obtain a generated type image reference beacon, analyzing the correlation between the generated type image reference beacon and the generated type image target, analyzing the correlation between the generated type target beacon image and the generated type initial image to obtain a beacon loss parameter, substituting the beacon loss parameter into the generated type image beacon loss formula to calculate the generated type image beacon loss, and verifying the loss degree of the generated type image beacon embedding.
2. The method for lossless construction of a beacon for a generated image according to claim 1, wherein: the set of generated initial images in S1 is a set of generated initial images, and the generated initial images are high-quality images.
3. The method for lossless construction of a beacon for a generated image according to claim 1, wherein the generated image beacon parameters in S2 are a gray level set, a color channel set, an image resolution set, an image texture and an image frequency, which are specifically as follows:
the color channel set is a beacon combination of red channel, green channel and blue channel values at a selected area, and the specific obtaining steps are as follows: according to the red channel, green channel and blue channel values, a color channel set is obtained through a formula, and the specific calculation formula is as follows
Wherein Color (Color channel), color (red channel), color (green channel) and Color (blue channel) are Color channel, red channel, green channel and blue channel values respectively,to round the symbol alpha 1 、β 1 And gamma 1 Coefficients of red channel, green channel and blue channel values, respectively, are calculated as
And->e is a natural constant;
the set of image resolutions is a numerical value of the image resolution.
4. The method for lossless construction of a beacon for a generated image according to claim 1, wherein the generated image target beacon parameters in S3 are a color channel set, an image resolution set and a gray frequency set, which is specifically as follows:
the gray frequency set is a set of frequency of each gray in the generated initial image and is obtained by a gray histogram;
the specific acquisition steps of the generated image target beacon are as follows: according to the Color channel set, the image Resolution set and the Gray frequency set, a generated image target beacon is obtained through a formula, wherein the specific calculation formula is XB (target beacon) = { Color (Color channel) } U { Resolution (image Resolution set) } U { Gray (Gray frequency set) }, and XB (target beacon), color (Color channel), resolution (image Resolution set) and Gray (Gray frequency set) are respectively the generated image target beacon, the Color channel set of the generated initial image, the image Resolution set of the generated initial image and the Gray frequency set of the generated initial image.
5. The method for lossless construction of a beacon for a generated image according to claim 1, wherein the generated image target beacon position parameters in S4 are image texture and image frequency, and the generated image target beacon position index is calculated from the generated image target beacon position parameters, which is specifically as follows:
The image texture is commonly measured by using entropy and angular second moment, and the specific obtaining steps are as follows: the image texture W is obtained by a formula according to the entropy E (x, y) and the angular second moment ASM (x, y) Texture and method for producing the same (x, y) the specific calculation formula ise is a natural constant;
the specific obtaining step of the generated image target beacon position index comprises the following steps: according to image texture W Texture and method for producing the same (x, y) obtaining a generated image target beacon position index by a formula, wherein the specific calculation formula is as followsWherein L (x, y) and P Frequency of (x, y) are the generated image target beacon location index and image frequency at (x, y) in the image, respectively;
the generated image target beacon position is the maximum generated image target beacon position index region of the reference region in the generated initial image, and the generated image reference region is the top left corner vertex, the bottom left corner vertex, the top right corner vertex, the bottom right corner vertex and the centroid of the generated initial image.
6. The method for constructing the beacon lossless for the generated image according to claim 1, wherein the generated image target beacon fault tolerance parameter in S5 is a generated image transformation fault tolerance parameter and a generated image target beacon code check code, which are specifically as follows:
The generated image transformation fault-tolerant parameter is a target frequency threshold of the generated initial image;
the generated image target beacon coding check code is a forward error correction code and a cyclic redundancy code.
7. The method for lossless construction of a beacon for a generated image according to claim 1, wherein the step S6 is performed to obtain the generated target beacon image, which comprises the following steps:
s61, frequency domain image acquisition: converting the generated initial image from a space domain to a frequency domain through Fourier transformation, and setting a generated image transformation fault tolerance parameter to obtain a frequency domain image;
s62, acquiring frequency domain data of a target beacon: carrying out Huffman coding on the generated image target beacon, and adding a generated image target beacon coding check code to obtain target beacon frequency domain data in a numerical form;
s63, embedding frequency domain data of the target beacon: embedding the target beacon frequency domain data into a generated image target beacon position of the frequency domain image to obtain a target beacon frequency domain image;
s64, generating target beacon image acquisition: and converting the target beacon frequency domain image from a frequency domain to a space domain through inverse Fourier transform to obtain a generated target beacon image.
8. The method for lossless construction of a beacon for a generated image according to claim 1, wherein the comparison verification with the target beacon for the generated image in S7 comprises the following steps:
s71, generating an image reference beacon extraction: converting the generated target beacon image from a space domain to a frequency domain through Fourier transformation, extracting target beacon frequency domain data, and decoding the target beacon frequency domain data through Huffman decoding to obtain a generated image reference beacon;
s72, calculating an image loss: analyzing the relation between the generated target beacon image and the generated initial image to obtain an image loss parameter, and calculating the image loss of the generated target beacon image and the generated initial image through an image loss formula;
s73, calculating a beacon loss: analyzing the relation between the generated image reference beacon and the generated image target beacon to obtain beacon loss parameters, and calculating the beacon loss of the generated image reference beacon and the generated image target beacon through a beacon loss formula;
s74, calculating a generated image beacon loss: and obtaining the generated image beacon loss from the image loss and the beacon loss.
9. The method for constructing a beacon lossless structure for a generated image according to claim 8, wherein the image loss and the beacon loss are calculated as follows:
The image loss parameters are typical evaluation indexes of the similarity of two images, namely structural similarity, perceptual hash and peak signal-to-noise ratio;
the specific image loss obtaining step comprises the following steps: according to the image loss parameters, obtaining the image loss through a formula, wherein the specific calculation formula is as followsWherein Loss is Image processing apparatus 、SSIM Image processing apparatus 、pHash Image processing apparatus And PNSR (PNSR) Image processing apparatus The method comprises the steps of respectively generating an image loss, structural similarity, perceptual hash and peak signal-to-noise ratio of a generated target beacon image and a generated initial image, wherein e is a natural constant;
the beacon loss parameters are typical evaluation indexes of two groups of data correlations, namely linear correlations, variances, standard deviations and kurtosis;
the specific acquisition steps of the beacon loss are as follows: according to the linear correlation, variance, standard deviation and kurtosis, the beacon loss is obtained through a formula, and the specific calculation formula is thatWherein Loss is Beacon beacon 、R1 Beacon beacon 、R2 Beacon beacon 、R3 Beacon beacon And R4 Beacon beacon Beacon loss, beacon linear correlation, beacon variance correlation, beacon standard deviation correlation and beacon kurtosis correlation of the generated image reference beacon and the generated image target beacon respectively;
the beacon variance correlation of the generated image reference beacon and the generated image target beacon is obtained specifically The method comprises the following steps: obtaining a beacon variance correlation R2 of the generated image reference beacon and the generated image target beacon by a formula according to the variance of the generated image reference beacon and the variance of the generated image target beacon Beacon beacon The specific calculation formula isWherein Sd Target beacon And Sd Reference beacon The variance of the generated image target beacon and the variance of the generated image reference beacon are respectively;
the specific beacon standard deviation correlation obtaining step of the generated image reference beacon and the generated image target beacon comprises the following steps: obtaining a beacon standard deviation correlation R3 of the generated image reference beacon and the generated image target beacon through a formula according to the standard deviation of the image reference beacon and the standard deviation of the generated image target beacon Beacon beacon The specific calculation formula isWherein Cov is Target beacon And Cov Reference beacon The standard deviation of the generated image target beacon and the standard deviation of the generated image reference beacon are respectively;
the specific obtaining step of the beacon kurtosis correlation of the generated image reference beacon and the generated image target beacon comprises the following steps: obtaining a beacon kurtosis correlation R4 of the generated image reference beacon and the generated image target beacon by a formula according to the kurtosis of the generated image reference beacon and the generated image target beacon Beacon beacon The specific calculation formula isWherein K is Target beacon And K Reference beacon Kurtosis of the generated image target beacon and kurtosis of the generated image reference beacon, respectively.
10. The method for generating image-oriented beacon lossless construction as recited in claim 8, whereinThe specific obtaining step of the generated image beacon loss comprises the following steps: obtaining the generated image beacon loss according to the image loss and the variance loss by a formula, wherein the specific calculation formula is as followsWherein Loss is Overall (L) Loss of beacons for the generated images.
CN202311302575.4A 2023-10-09 2023-10-09 Beacon lossless construction method for generated image Pending CN117408861A (en)

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