CN115187444A - Image tracing information safety protection method and device and electronic equipment - Google Patents
Image tracing information safety protection method and device and electronic equipment Download PDFInfo
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Abstract
The application provides an image traceability information security protection method, device and electronic equipment, wherein the method comprises the following steps: acquiring an original image of tracing information of watermarks to be superposed and a preset superposed image watermark; adjusting the characteristic parameters of the preset superposed image watermarks according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set; superposing each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested; carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested; and determining an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested. The method can effectively resist the removal attack of the intelligent image information.
Description
Technical Field
The application relates to the field of information security, in particular to an image traceability information security protection method and device and electronic equipment.
Background
Digital watermarking is widely applied to scenes such as multimedia data transmission, release, sharing and the like as an effective means for true and false identification and copyright protection.
However, the existing digital watermarks, especially image watermarks, suffer from a large amount of intelligent watermark information removal attacks based on image restoration technology, so that most of the existing visible watermark mechanisms fail. This has a serious impact on the copyright protection and traceability of images.
Therefore, it is desirable to provide a security method that can successfully defend against such an attack of removing the intelligent image information.
Disclosure of Invention
In view of this, the present application provides an image tracing information security protection method and apparatus, and an electronic device.
Specifically, the method is realized through the following technical scheme:
according to a first aspect of the embodiments of the present application, there is provided an image tracing information security protection method, including:
acquiring an original image of tracing information of watermarks to be superposed and a preset superposed image watermark;
adjusting the characteristic parameters of the preset superposed image watermarks according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises an adjustment range of the characteristic parameters;
superposing each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested;
carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested;
and determining an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
According to a second aspect of the embodiments of the present application, there is provided an image tracing information security protection apparatus, including:
the system comprises an acquisition unit, a comparison unit and a comparison unit, wherein the acquisition unit is used for acquiring an original image of tracing information of watermarks to be superposed and a preset superposed image watermark;
the adjusting unit is used for adjusting the characteristic parameters of the preset superposed image watermarks according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises an adjustment range of the characteristic parameters;
the superposition unit is used for superposing each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested;
the testing unit is used for carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack testing model to obtain an output image corresponding to each watermark image to be tested;
and the determining unit is used for determining the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
According to a third aspect of embodiments herein, there is provided an electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor, the processor being configured to execute the machine executable instructions to implement the method provided by the first aspect.
According to the image traceability information security protection, before image watermark superposition is carried out on an original image, pixel-level color parameter adjustment is carried out on the image watermarks to be superposed according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set, each image watermark in the adjusted image watermark set is superposed with the original image to obtain a watermark image set to be tested, an attack test model is removed by utilizing preset intelligent image information, each watermark image to be tested in the watermark image set to be tested is subjected to intelligent watermark removal attack to obtain an output image corresponding to each watermark image to be tested, further, according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, an optimal watermark image and an optimal image watermark are determined, through setting image watermark characteristic parameter constraint conditions, according to the set constraint conditions, pixel-level characteristic parameter adjustment is carried out on the image watermarks to be superposed, on the basis that modification of the preset image traceability information is reduced as much as possible, the intelligibility and copyright content integrity of the preset image traceability information can be effectively resisted, and the intelligent image watermark removal attack information robustness can be greatly improved.
Drawings
Fig. 1 is a schematic flowchart of an image tracing information security protection method according to an exemplary embodiment of the present application;
fig. 2 is a schematic flowchart illustrating an image traceability information security protection method according to an exemplary embodiment of the present application;
fig. 3 is a schematic structural diagram of an image traceability information security apparatus according to an exemplary embodiment of the present application;
fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
In order to make the technical solutions provided in the embodiments of the present application better understood and make the above objects, features and advantages of the embodiments of the present application more comprehensible, the technical solutions in the embodiments of the present application are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a schematic flow chart of an image traceability information security protection method provided in an embodiment of the present application is shown, as shown in fig. 1, the image traceability information security protection method may include the following steps:
and S100, obtaining an original image of tracing information of the watermark to be superposed and a preset superposed image watermark.
Step S110, adjusting the feature parameters of the image watermarks to be superposed according to the preset constraint conditions of the feature parameters of the image watermarks to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises the adjustment range of the characteristic parameters.
Illustratively, the image watermark of the predetermined superposition may include, but is not limited to, a picture, a character string, and the like.
In the embodiment of the application, in order to improve the performance of resisting removal attack of the image watermark in the watermark image, before the obtained image watermark to be superposed is superposed with the original image, the characteristic parameters of the image watermark may be adjusted.
In addition, in order to refine the adjustment granularity of the feature parameters of the image watermark, when the feature parameters of the image watermark are adjusted, the adjustment is not limited to the adjustment of the whole image watermark, but the color parameters of the image watermark can be adjusted at the pixel level, that is, the color parameters of each pixel of the image watermark can be adjusted separately.
Illustratively, the color parameters may include, but are not limited to, some or all of color parameters and transparency.
Illustratively, the color parameters may include, but are not limited to, RGB (Red, green, blue, red-Blue-Green) values, YUV (a color coding scheme) values, ycbCr (a color coding scheme) values, or the like.
In the embodiment of the application, an image watermark characteristic parameter constraint condition, that is, an adjustment range of an image watermark characteristic parameter may be preset, and in the process of adjusting the characteristic parameter of the image watermark, the characteristic parameter of the image watermark to be superposed needs to be adjusted according to the preset image watermark parameter constraint condition, that is, the characteristic parameter of the image watermark to be superposed is adjusted within the preset characteristic parameter adjustment range, and the value of the adjusted characteristic parameter of the image watermark is controlled within the preset value range.
For example, taking transparency adjustment as an example, when the transparency of any pixel of the image watermark is adjusted, it is necessary to ensure that the adjusted transparency of the pixel is within a preset range, for example, greater than or equal to a preset transparency threshold.
For example, for a predetermined superimposed image watermark, a plurality of different adjusted image watermarks may be obtained according to the above feature parameter adjustment manner. The characteristic parameters of different adjusted image watermarks are different.
Optionally, in this embodiment of the present application, the image watermarks in the adjusted image watermark set may also include a predetermined superimposed image watermark.
And step S120, overlapping each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested.
In the embodiment of the present application, under the condition that the adjusted image watermark set is obtained in the manner described in the above embodiment, for any image watermark in the adjusted image watermark set, the image watermark may be superimposed on the original image to obtain the corresponding watermark image to be tested.
Step S130, carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested.
In the embodiment of the application, in order to ensure the removal attack resistance of the adjusted image watermark, a preset intelligent image information removal attack test mode can be utilized to perform intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested, so as to obtain an output image after the intelligent watermark removal attack corresponding to each watermark image to be tested.
Illustratively, the intelligent image information removal attack test model may be a white box model, a gray box model, or a black box model.
Illustratively, the intelligent image information removal attack test model may be an open source model or a commercial model.
Illustratively, the intelligent image information removal attack testing model provides an input and output interface, the input can include a watermark image to be tested, and the output result includes the watermark image after the intelligent watermark removal attack.
Step S140, determining an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
In the embodiment of the present application, when the output image corresponding to each watermark image to be tested is obtained in the manner described in the above embodiment, the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested may be determined, and the watermark image with the best watermark removal attack resistance effect (referred to as an optimal watermark image herein) and the optimal image watermark may be determined according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
Illustratively, the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested can be characterized by a Peak Signal-to-Noise Ratio (PSNR) and/or a Structural SIMilarity (SSIM) between the watermark image to be tested and the corresponding output image.
Illustratively, the larger the PSNR between the watermark image to be tested and the corresponding output image, the smaller the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
Illustratively, the greater the structural similarity between the watermark image to be tested and the corresponding output image, the smaller the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested is.
For example, the smaller the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested is, the smaller the change of the image is after the watermark image to be tested is subjected to the intelligent watermark removal attack by the intelligent image information removal attack test model, that is, the better the watermark removal attack resisting effect of the watermark image to be tested is.
Accordingly, the optimal watermark image may be the watermark image to be tested having the smallest difference with the corresponding output image.
It can be seen that, in the method flow shown in fig. 1, before image watermark superposition is performed on an original image, pixel-level color parameter adjustment is performed on a predetermined superposed image watermark according to a preset image watermark characteristic parameter constraint condition to obtain an adjusted image watermark set, each image watermark in the adjusted image watermark set is superposed with the original image to obtain a to-be-tested watermark image set, an attack test model is removed by using preset intelligent image information, an intelligent watermark removal attack is performed on each to-be-tested watermark image in the to-be-tested watermark image set to obtain an output image corresponding to each to-be-tested watermark image, further, an optimal watermark image and an optimal image watermark are determined according to a difference between the to-be-tested watermark image and the output image corresponding to the to-be-tested watermark image, pixel-level characteristic parameter adjustment is performed on the predetermined superposed image watermark according to the set constraint condition, and on the basis of reducing modification of predetermined image tracing information as much as possible, intelligibility and content integrity of the predetermined image tracing information can be ensured, and intelligent image watermark removal attack can be effectively resisted, and traceability robustness can be greatly improved.
In some embodiments, in step S110, adjusting the feature parameter of the image watermark to be pre-superimposed according to the preset constraint condition of the feature parameter of the image watermark may include:
and adjusting the color parameters of part or all pixels of the image watermark to be superposed according to the preset constraint conditions of the characteristic parameters of the image watermark.
For example, in order to improve flexibility of adjusting the characteristic parameters of the image watermark, in the case of adjusting the characteristic parameters of the image watermark, the color parameters of some or all pixels of the image watermark to be superimposed may be adjusted according to a preset constraint condition of the characteristic parameters of the image watermark.
In one example, the characteristic parameters may further include: some or all of the spatial parameters, morphological parameters, and numerical parameters.
For example, in order to improve the effect of adjusting the characteristic parameters of the image watermark and optimize the performance of resisting watermark removal attack of the watermark image corresponding to the image watermark after the characteristic parameters are adjusted, when the characteristic parameters of the image watermark are adjusted, besides the adjustment of the color parameters at the pixel level, the spatial parameters, the morphological parameters and the quantity parameters of the image watermark can be partially or completely adjusted.
For example, the spatial parameters of the image watermark may include a position where the image watermark is superimposed into the image, such as a position of a center coordinate of the image watermark in the image.
The morphological parameters of the image watermark may include the size and rotation angle of the image watermark, and the like.
The number of image watermarks parameter may predetermine the number of superimposed image watermarks.
Accordingly, the image watermark characteristic parameter constraints may further include constraints for some or all of the spatial parameters, the morphological parameters, and the number parameters.
For example, constraints on the spatial parameters include that the central coordinate is located in a particular region, etc., e.g., x max >x>x min And y max >y>y min 。
Constraint strip of morphological parametersThe elements including said image size satisfying upper and lower limit requirements, e.g. u max >u>u min And v max >v>v min . Where u may be the length of the image watermark and v may be the width of the image watermark.
The constraint condition of the quantity parameter comprises that the number of the image watermarks satisfies n max >n>n min 。
In the embodiment of the present application, when the number of image watermarks to be superimposed is plural, the spatial parameter, the morphological parameter, and the color parameter of each image watermark may be different.
In some embodiments, in step S110, adjusting the feature parameter of the image watermark to be pre-superimposed according to a preset constraint condition of the image watermark feature parameter to obtain an adjusted image watermark set, which may include:
adjusting the characteristic parameters of the image watermarks to be superposed according to the preset image watermark characteristic parameter constraint conditions and a preset optimization target to obtain an adjusted image watermark set meeting the preset optimization target;
the preset optimization target comprises a first watermark image and a second watermark image, wherein the difference between the first watermark image and the second watermark image is smaller than a first threshold value; the first watermark image is a watermark image obtained by superposing a preset superposed image watermark and an original image, and the second watermark image is a watermark image obtained by superposing an image watermark with adjusted characteristic parameters and the original image.
Illustratively, in order to ensure the understandability of the image watermark after the characteristic parameter adjustment, an optimization target may be preset for the adjustment of the characteristic parameter of the image watermark, and the optimization target may constrain the overall adjustment effect of the characteristic parameter of the image watermark so as to avoid an excessive difference between watermark images obtained by superimposing the image watermark before and after the characteristic parameter adjustment on the original image.
Correspondingly, when the feature parameters of the image watermark are adjusted, the feature parameters of the image watermark to be superposed can be adjusted according to the preset image watermark feature parameter constraint condition and the preset optimization target, so that an adjusted image watermark set meeting the preset optimization target is obtained.
Illustratively, for any image watermark (feature parameter adjusted image watermark) in the adjusted image watermark set that meets the preset optimization target, a difference between a watermark image (referred to as a second watermark image herein) obtained by superimposing the image watermark and the original image, and a watermark image (referred to as a first watermark image herein) obtained by superimposing the predetermined superimposed image watermark (feature parameter adjusted image watermark) and the original image is smaller than a preset threshold (referred to as a first threshold herein).
For example, the difference between the first watermark image and the second watermark image may be characterized by a peak signal-to-noise ratio and/or a structural similarity between the first watermark image and the second watermark image.
In some embodiments, the method for performing intelligent watermark removal attack on each watermark image to be tested in a watermark image set to be tested by using a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested includes:
for any watermark image to be tested, respectively inputting the watermark image to be tested to N preset intelligent image information removal attack test models, and acquiring an output image after the intelligent watermark corresponding to the watermark image to be tested is removed; wherein N is more than or equal to 1.
Illustratively, N intelligent image information removal attack test models can be preset for performing an intelligent watermark removal attack test on the watermark image to be tested so as to test the watermark removal attack resistance of the watermark image to be tested.
Exemplarily, for any watermark image to be tested, the watermark image to be tested can be respectively input into N preset intelligent image information attack removal test models, and an output image after an intelligent watermark attack is removed corresponding to the watermark image to be tested is obtained.
For example, in consideration of the fact that a model for performing watermark removal attack on a watermark image is not fixed in an actual application scenario, in order to better test the watermark removal attack resistance of the watermark image to be tested and optimize the watermark removal attack resistance of a finally obtained watermark image (such as the optimal watermark image), a plurality of intelligent image information removal attack test models may be preset, and the intelligent image information removal attack test models are used to perform intelligent watermark removal attack test on each watermark image to be tested.
That is, preferably, N.gtoreq.2.
In some embodiments, the determining the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested may include:
according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, which has the smallest difference with the corresponding output image and the difference with the corresponding output image is smaller than a second threshold value, is determined as the optimal watermark image, and the image watermark superposed in the optimal watermark image is determined as the optimal image watermark.
For example, in order to ensure the watermark removal attack resistance of the image watermark after the characteristic parameter adjustment, when the watermark removal attack resistance test is performed on the watermark image to be tested by using the intelligent image information removal attack test model, the optimal image watermark may be determined from the watermark image to be tested whose difference with the corresponding output image is smaller than a preset threshold (referred to as a second threshold herein).
For example, the watermark image to be tested, which has the smallest difference with the corresponding output image and the difference with the corresponding output image smaller than the second threshold value, may be determined as the optimal watermark image, and the image watermark superimposed in the optimal watermark image may be determined as the optimal image watermark.
For example, the second threshold may be smaller than the first threshold.
In one example, the difference between the watermark image to be tested and the corresponding output image being less than the second threshold may be determined by:
under the condition that the preset intelligent image information removal attack test model comprises a plurality of intelligent image information removal attack test models, for any watermark image to be tested, if the difference between the watermark image to be tested and each corresponding output image is smaller than a second threshold value, determining that the difference between the watermark image to be tested and the corresponding output image is smaller than the second threshold value; and carrying out intelligent watermark removal attack on the watermark image to be tested through different intelligent image information removal attack test models to obtain different output images corresponding to the watermark image to be tested.
Exemplarily, under the condition that the preset intelligent image information removal attack test model comprises a plurality of intelligent image information removal attack test models, for any watermark image to be tested, the watermark image to be tested can be respectively input into the plurality of intelligent image information removal attack test models to obtain a multi-frame output image corresponding to the watermark image to be tested, and the difference between the watermark image to be tested and each corresponding output image is respectively determined.
For any one to-be-tested watermark image, if the difference between the to-be-tested watermark image and each corresponding output image is smaller than a second threshold, it is determined that the difference between the to-be-tested watermark image and the corresponding output image is smaller than the second threshold.
For example, in a case where the preset intelligent image information removal attack test model includes a plurality of intelligent image information removal attack test models, when determining a watermark image to be tested having a minimum difference with a corresponding output image, for any watermark image to be tested, a difference between the watermark image to be tested and the corresponding output image may be determined according to a difference between the watermark image to be tested and each corresponding output image.
For example, for any watermark image to be tested, an average value of differences between the watermark image to be tested and corresponding output images can be determined as a difference between the watermark image to be tested and the corresponding output images.
Accordingly, the difference from the corresponding output image may be smallest as an average value of the differences from the corresponding output images.
For example, assuming that the preset intelligent image information removal attack test model includes three intelligent image information removal attack test models, for the watermark image a to be tested, the corresponding output images thereof are output images A1, A2, and A3, the difference between the watermark image a to be tested and the output image A1 is C1, the difference between the watermark image a to be tested and the output image A2 is C2, and the difference between the watermark image a to be tested and the output image A3 is C3, the difference between the watermark image a to be tested and the corresponding output image may be an average value of C1, C2, and C3.
Illustratively, the average may be an arithmetic average or a weighted average.
For example, in the case that the average value is a weighted average value, the weighting coefficient of the difference between the watermark image to be tested and each corresponding output image may be set according to the attack performance of the intelligent image information removal attack test model.
Still taking the above example as an example, assuming that the three intelligent image information removal attack test models are M1, M2 and M3, respectively, the weighting coefficients of C1, C2 and C3 can be set according to the attack performance of M1, M2 and M3.
Illustratively, the more aggressive the intelligent image information removal attack test model, the larger the weighting factor.
For example, the attack performance of the intelligent image information removal attack test model may be determined according to the attack test result of the intelligent image information removal attack test model on the test set.
For example, for any intelligent image information removal attack test model, each test watermark image in the test set may be respectively input to the intelligent image information removal attack test model to obtain an output image corresponding to each test watermark image, an arithmetic mean of differences between each test watermark image and the corresponding output image is counted, and the arithmetic mean is determined as a test difference value of the intelligent image information removal attack test model. Furthermore, the attack performance of each intelligent image information removal attack test model can be determined according to the test difference values corresponding to different intelligent image information removal attack test models.
Illustratively, the larger the corresponding test difference value is, the stronger the attack performance of the intelligent image information removal attack test model is.
In one implementation, the determining an optimal watermark image and an optimal image watermark according to a difference between the watermark image to be tested and an output image corresponding to the watermark image to be tested may further include:
and if the watermark images to be tested, which have the difference with the corresponding output images smaller than the second threshold value, do not exist, and the iteration times do not reach the maximum iteration times, generating a new adjusted image watermark set according to a preset image watermark optimization algorithm and the corresponding output images of the watermark images to be tested, and determining the new optimal watermark images and the optimal image watermarks.
For example, in an actual scene, it is considered that in the watermark image to be tested obtained after the feature parameter adjustment is performed on the image watermark in the above manner, there may not be a watermark image to be tested whose difference with the corresponding output image is smaller than the second threshold. Therefore, in order to optimize the watermark removal attack resistance of the finally determined optimal watermark image, the adjusted characteristic parameter value determined when the characteristic parameter of the image watermark is adjusted can be iteratively optimized.
Correspondingly, for any iteration process, if the image watermark is subjected to characteristic parameter adjustment according to the characteristic parameter values determined in the iteration process, the watermark image to be tested, which has the difference with each corresponding output image smaller than the second threshold value, does not exist in the watermark image to be tested, and whether the current iteration number reaches the preset maximum iteration number can be determined.
And if the iteration times do not reach the maximum iteration times, generating a new adjusted image watermark set according to a preset image watermark optimization algorithm and the corresponding output images of the watermark images to be tested, and determining the optimal watermark image and the optimal image watermark in a new round, namely entering the new round of iteration (adding 1 to the iteration times).
Respectively superposing each image watermark in the newly adjusted image watermark set with the original image to obtain a new watermark image set to be tested; and carrying out intelligent watermark removal attack on each watermark image to be tested in the new watermark image set to be tested by utilizing a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested, determining whether a watermark image to be tested with the difference smaller than a second threshold value exists or not according to the difference between the new watermark image to be tested and the output image corresponding to the watermark image to be tested, if so, determining the watermark image to be tested with the difference between the corresponding output image and the watermark image to be tested with the difference smaller than the second threshold value as an optimal watermark image, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark.
Illustratively, if there is no to-be-tested watermark image with the difference between the to-be-tested watermark image and each corresponding output image smaller than the second threshold, and the iteration number reaches the maximum iteration number, determining the to-be-tested watermark image with the most intelligent image information removal attack test model attack failure as the optimal watermark image, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark.
Correspondingly, for any watermark image to be tested, whether the watermark image to be tested enables the corresponding intelligent image information removal attack test model to be attacked and failed or not can be determined according to the difference between the watermark image to be tested and each corresponding output image, and the intelligent image information removal attack test model which attacks and fails on the watermark image to be tested in a plurality of preset intelligent image information removal attack test models is counted.
Illustratively, for any watermark image to be tested and any intelligent image information removal attack test model, if the difference between an output image obtained by removing the attack test model from the watermark image to be tested by using the intelligent image information removal attack test model and the watermark image to be tested is smaller than the second threshold value, the watermark image to be tested is determined to enable the intelligent image information removal attack test model to attack failure (namely the intelligent image information removal attack test model attacks the watermark image to be tested failure).
In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below with reference to specific examples.
In the embodiment, aiming at the actual requirement of image tracing information security protection, an image tracing information security protection method is provided, and the color parameters of the image tracing information are adjusted at a pixel level fine granularity, and a plurality of dimensions such as the space parameters, the form parameters and the quantity parameters of the image watermark are considered, so that the robust image watermark resisting watermark removal attack is generated and optimized under a specific constraint condition. In the generation and optimization process, at least one input/output interface of an intelligent image information removal attack algorithm or model can be combined, the watermark image generated by iteration is continuously input under a specific constraint condition, the intelligent watermark removal output result of the watermark image is obtained, and the modification and optimization of the watermark image are carried out according to the feedback output result.
The following describes an implementation flow of the image tracing information security protection method provided in this embodiment.
As shown in fig. 2, an implementation flow of the image tracing information security protection method provided in this embodiment may include the following steps:
s1: and acquiring an original image of the tracing information of the watermark to be superposed and the image watermark to be superposed.
Illustratively, the image watermark may be a picture, a string of characters, or the like.
S2: and acquiring the characteristic parameter constraint condition and the optimization target of the predetermined image watermark.
Illustratively, the feature parameters may include spatial parameters, pixel level color parameters, morphological parameters, number parameters, and the like.
For example, the spatial parameters may include position information of the image watermark superimposed into the image, such as center coordinates (x, y) of the image watermark; the color parameters may include the RGB values of each pixel in the image watermark and its transparency (r, g, b, α); the morphological parameters may include the size (u, v) (length and width) and the rotation angle θ of the image watermark; the quantity parameter may comprise a predetermined number n of superimposed image watermarks.
For example, constraints on spatial parameters include that the central coordinate is located within a particular region, etc., e.g., x max >x>x min And y max >y>y min 。
The constraints of the morphological parameters include that the size of the image satisfies upper and lower limit requirements, e.g. u max >u>u min And v max >v>v min . Where u may be the length of the image watermark and v may be the width of the image watermark.
The constraint condition of the quantity parameter comprises that the number of the image watermarks satisfies n max >n>n min 。
Illustratively, the image watermark optimization target includes that a difference between a watermark image obtained by superimposing the image watermark with the adjusted characteristic parameters on the original image (i.e., the second watermark image) and a watermark image obtained by superimposing the image watermark with the predetermined image is smaller than a preset threshold (e.g., the first threshold).
For example, the norm of the difference between the two (i.e., the difference between the first watermark image and the second watermark image) is less than epsilon.
Illustratively, ε is a preset value; the norm may include a1 norm, a2 norm, or an infinite norm.
S3: and randomly generating an image watermark set (namely the adjusted image watermark set) meeting the constraint condition, and superposing the image watermark set and the original image one by one to generate a watermark image set to be detected.
Illustratively, each adjusted image watermark is obtained by modifying the image watermark which is preset to be superposed by the randomly adjusted characteristic parameter.
S4: and inputting each watermark image to be tested in the watermark image set to be tested into N (N is more than or equal to 1) intelligent image information removal attack test models one by one to obtain an output image set after the intelligent watermark removal attack. If the optimization finishing condition is met, outputting the optimal watermark image in the watermark image set and the image watermark corresponding to the optimal watermark image; otherwise, go to step S5.
Illustratively, the intelligent image information removal attack test model may be a white box model, a gray box model, or a black box model.
Illustratively, the intelligent image information removal attack test model may be an open source model or a commercial model.
Illustratively, the intelligent image information removal attack testing model provides an input and output interface, the input can include a watermark image to be tested, and the output result includes the watermark image after the intelligent watermark removal attack.
For example, the optimization ending condition may include that the optimization process reaches a maximum number of iterations, or/and any watermark image in the generated to-be-detected watermark image set meets a preset requirement.
For example, the preset requirements may include: any watermark image exists in the generated watermark image set to be tested, so that the difference between all output images of the N intelligent image information removal attack test models and the input watermark image (namely the watermark image to be tested) is smaller than a preset threshold value (such as the second threshold value).
It should be noted that, in the embodiment of the present application, in order to optimize the watermark removal attack resistance of the determined optimal watermark image as much as possible, when any watermark image in the generated watermark image set to be tested meets the preset requirement, if the iteration number does not reach the maximum iteration number, a new adjusted image watermark set is generated according to the preset image watermark optimization algorithm and the output image corresponding to each watermark image to be tested, and a new round of optimal watermark image and optimal image watermark determination is performed until the iteration number reaches the maximum iteration number, and the final optimal watermark image and optimal image watermark are determined.
S5: and calculating a characteristic parameter adjustment value of the image watermarks in the set to be detected according to a preset image watermark optimization algorithm and the output image set, regenerating the watermark image set to be detected, and entering S4.
Illustratively, the preset image watermark optimization algorithm includes a particle swarm algorithm, a genetic algorithm, a differential evolution algorithm, a bayesian optimization algorithm or a simulated annealing algorithm.
For example, the particle swarm algorithm may generate an optimized image watermark by continuously iteratively updating the position and speed of each particle in a preset number of populations.
Illustratively, the particles may be a combined representation of some or all of the characteristic parameters of the image watermark.
For example, the genetic algorithm can continuously modify the generated image watermark iteratively through three main steps of selection, intersection and mutation.
The methods provided herein are described above. The following describes the apparatus provided in the present application:
referring to fig. 3, a schematic structural diagram of an image traceability information security apparatus provided in an embodiment of the present application is shown in fig. 3, where the image traceability information security apparatus may include:
an obtaining unit 310, configured to obtain an original image of tracing information of a watermark to be superimposed and a predetermined superimposed image watermark;
an adjusting unit 320, configured to adjust the feature parameters of the image watermarks to be superimposed according to a preset constraint condition of the feature parameters of the image watermarks to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises an adjustment range of the characteristic parameters;
an overlapping unit 330, configured to overlap each image watermark in the adjusted image watermark set with the original image, respectively, to obtain a to-be-tested watermark image set;
the testing unit 340 is configured to perform an intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by using a preset intelligent image information removal attack testing model to obtain an output image corresponding to each watermark image to be tested;
the determining unit 350 is configured to determine an optimal watermark image and an optimal image watermark according to a difference between the to-be-tested watermark image and an output image corresponding to the to-be-tested watermark image.
In some embodiments, the adjusting unit 320 adjusts the feature parameter of the predetermined superimposed image watermark according to a preset constraint condition of the feature parameter of the image watermark, including:
and adjusting the color parameters of part or all pixels of the preset superposed image watermark according to a preset image watermark characteristic parameter constraint condition.
In some embodiments, the characteristic parameters further include: some or all of the spatial parameters, morphological parameters, and numerical parameters.
In some embodiments, the adjusting unit 320 adjusts the feature parameters of the predetermined superimposed image watermark according to a preset image watermark feature parameter constraint condition, to obtain an adjusted image watermark set, including:
adjusting the characteristic parameters of the image watermarks to be superposed according to a preset image watermark characteristic parameter constraint condition and a preset optimization target to obtain an adjusted image watermark set meeting the preset optimization target;
wherein the preset optimization target comprises that the difference between the first watermark image and the second watermark image is smaller than a first threshold value; the first watermark image is a watermark image obtained by superposing the image watermark which is preset to be superposed with the original image, and the second watermark image is a watermark image obtained by superposing the image watermark with the adjusted characteristic parameters with the original image.
In some embodiments, the performing, by the testing unit 340, an intelligent watermark removal attack on each to-be-tested watermark image in the to-be-tested watermark image set by using a preset intelligent image information removal attack testing model to obtain an output image corresponding to each to-be-tested watermark image, includes:
for any watermark image to be tested, respectively inputting the watermark image to be tested to N preset intelligent image information removal attack test models, and acquiring an output image after the intelligent watermark corresponding to the watermark image to be tested is removed; wherein N is more than or equal to 1.
In some embodiments, the determining unit 350 determines the optimal watermark image and the optimal image watermark according to the difference between the image to be tested and the output image corresponding to the image to be tested, including:
and determining the watermark image to be tested with the smallest difference with the corresponding output image and the difference with the corresponding output image smaller than a second threshold value as the optimal watermark image according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark.
In some embodiments, the testing unit 340 determines that the difference between the watermark image to be tested and the corresponding output image is less than the second threshold by:
under the condition that the preset intelligent image information removal attack test model comprises a plurality of intelligent image information removal attack test models, for any watermark image to be tested, if the difference between the watermark image to be tested and each corresponding output image is smaller than a second threshold value, determining that the difference between the watermark image to be tested and the corresponding output image is smaller than the second threshold value; and carrying out intelligent watermark removal attack on the watermark image to be tested through different intelligent image information removal attack test models to obtain different output images corresponding to the watermark image to be tested.
In some embodiments, the determining unit 350 determines the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and further includes:
and if the watermark images to be tested, which have the difference with the corresponding output images smaller than the second threshold value, do not exist, and the iteration times do not reach the maximum iteration times, generating a new adjusted image watermark set according to a preset image watermark optimization algorithm and the corresponding output images of the watermark images to be tested, and determining the new optimal watermark images and the optimal image watermarks.
In some embodiments, the determining unit 350 determines the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and further includes:
if the watermark image to be tested with the difference between the watermark image to be tested and each corresponding output image smaller than the second threshold does not exist, and the iteration frequency reaches the maximum iteration frequency, determining the watermark image to be tested with the most intelligent image information removal attack test model attack failure as the optimal watermark image, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark;
and if the difference between an output image obtained by removing the attack test model from the watermark image to be tested by using the intelligent image information removal attack test model and the watermark image to be tested is smaller than the second threshold value, determining that the watermark image to be tested makes the attack of the intelligent image information removal attack test model invalid.
An embodiment of the present application provides an electronic device, which includes a processor and a memory, where the memory stores machine executable instructions that can be executed by the processor, and the processor is configured to execute the machine executable instructions, so as to implement the image tracing information security protection method described above.
Please refer to fig. 4, which is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present disclosure. The electronic device may include a processor 401, a memory 402 having stored thereon machine executable instructions. The processor 401 and memory 402 may communicate via a system bus 403. Moreover, the processor 401 may execute the image tracing information security protection method described above by reading and executing machine executable instructions corresponding to the image tracing information security protection logic in the memory 402.
The memory 402 referred to herein may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: RAM (random Access Memory), volatile Memory, non-volatile Memory, flash Memory, a storage drive (e.g., a hard drive), a solid state drive, any type of storage disk (e.g., an optical disk, a dvd, etc.), or similar storage medium, or a combination thereof.
In some embodiments, a storage medium, such as the memory 402 in fig. 4, is also provided, and is a machine-readable storage medium, in which machine-executable instructions are stored, and when executed by a processor, implement the image tracing information security protection method described above. For example, the storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (12)
1. An image tracing information security protection method is characterized by comprising the following steps:
acquiring an original image of tracing information of watermarks to be superposed and a preset superposed image watermark;
adjusting the characteristic parameters of the preset superposed image watermarks according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises an adjustment range of the characteristic parameters;
superposing each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested;
carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested;
and determining an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
2. The method according to claim 1, wherein the adjusting the feature parameter of the predetermined superimposed image watermark according to a preset image watermark feature parameter constraint condition comprises:
and adjusting the color parameters of part or all pixels of the preset superposed image watermark according to a preset image watermark characteristic parameter constraint condition.
3. The method according to claim 1 or 2, wherein the characteristic parameters further comprise: some or all of the spatial parameters, morphological parameters, and numerical parameters.
4. The method according to claim 1, wherein the adjusting the feature parameters of the predetermined image watermark to be superimposed according to a preset constraint condition of the feature parameters of the image watermark to obtain an adjusted set of image watermarks comprises:
adjusting the characteristic parameters of the image watermarks to be superposed according to a preset image watermark characteristic parameter constraint condition and a preset optimization target to obtain an adjusted image watermark set meeting the preset optimization target;
wherein the preset optimization target comprises that the difference between the first watermark image and the second watermark image is smaller than a first threshold value; the first watermark image is a watermark image obtained by superposing the image watermark which is preset to be superposed with the original image, and the second watermark image is a watermark image obtained by superposing the image watermark with the adjusted characteristic parameters with the original image.
5. The method according to claim 1, wherein the performing an intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by using a preset intelligent image information removal attack test model to obtain an output image corresponding to each watermark image to be tested comprises:
for any watermark image to be tested, respectively inputting the watermark image to be tested to N preset intelligent image information removal attack test models, and acquiring an output image after the intelligent watermark corresponding to the watermark image to be tested is removed; wherein N is more than or equal to 1.
6. The method of claim 1, wherein determining the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested comprises:
and determining the watermark image to be tested with the smallest difference with the corresponding output image and the difference with the corresponding output image smaller than a second threshold value as the optimal watermark image according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark.
7. The method of claim 6, wherein the difference between the image of the watermark to be tested and the corresponding output image is less than a second threshold value is determined by:
under the condition that the preset intelligent image information removal attack test model comprises a plurality of intelligent image information removal attack test models, for any watermark image to be tested, if the difference between the watermark image to be tested and each corresponding output image is smaller than a second threshold value, determining that the difference between the watermark image to be tested and the corresponding output image is smaller than the second threshold value; and carrying out intelligent watermark removal attack on the watermark image to be tested through different intelligent image information removal attack test models to obtain different output images corresponding to the watermark image to be tested.
8. The method of claim 7, wherein determining the optimal watermark image and the optimal image watermark according to a difference between the watermark image to be tested and an output image corresponding to the watermark image to be tested, further comprises:
and if the watermark images to be tested, which have the difference with the corresponding output images smaller than the second threshold value, do not exist, and the iteration times do not reach the maximum iteration times, generating a new adjusted image watermark set according to a preset image watermark optimization algorithm and the corresponding output images of the watermark images to be tested, and determining the new optimal watermark images and the optimal image watermarks.
9. The method of claim 7, wherein determining the optimal watermark image and the optimal image watermark according to a difference between the watermark image to be tested and an output image corresponding to the watermark image to be tested, further comprises:
if the watermark images to be tested, the differences between which and the corresponding output images are smaller than a second threshold value, do not exist, and the iteration times reach the maximum iteration times, determining the watermark images to be tested, the most of which are invalid in the attack of the intelligent image information removal attack test model, as the optimal watermark images, and determining the image watermarks superposed in the optimal watermark images as the optimal image watermarks;
and if the difference between an output image obtained by removing the attack test model from the watermark image to be tested by using the intelligent image information removal attack test model and the watermark image to be tested is smaller than the second threshold value, determining the watermark image to be tested to enable the intelligent image information removal attack test model to be attacked to be invalid.
10. An image traceability information security protection device, comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring an original image of tracing information of watermarks to be superposed and an image watermark to be superposed;
the adjusting unit is used for adjusting the characteristic parameters of the preset superposed image watermarks according to preset image watermark characteristic parameter constraint conditions to obtain an adjusted image watermark set; the characteristic parameters comprise color parameters at the pixel level, and the characteristic parameter constraint condition comprises an adjustment range of the characteristic parameters;
the superposition unit is used for superposing each image watermark in the adjusted image watermark set with the original image to obtain a watermark image set to be tested;
the testing unit is used for carrying out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested by utilizing a preset intelligent image information removal attack testing model to obtain an output image corresponding to each watermark image to be tested;
and the determining unit is used for determining the optimal watermark image and the optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested.
11. The apparatus according to claim 10, wherein the adjusting unit adjusts the feature parameter of the predetermined superimposed image watermark according to a preset image watermark feature parameter constraint condition, and the adjusting unit includes:
adjusting color parameters of part or all pixels of the image watermark to be superposed according to a preset image watermark characteristic parameter constraint condition;
wherein the characteristic parameters further include: some or all of the spatial parameters, morphological parameters, and quantitative parameters;
the adjusting unit adjusts the feature parameters of the image watermarks to be superposed according to a preset image watermark feature parameter constraint condition to obtain an adjusted image watermark set, and the adjusting unit includes:
adjusting the characteristic parameters of the image watermarks to be superposed according to a preset image watermark characteristic parameter constraint condition and a preset optimization target to obtain an adjusted image watermark set meeting the preset optimization target;
wherein the preset optimization target comprises that the difference between the first watermark image and the second watermark image is smaller than a first threshold value; the first watermark image is a watermark image obtained by superposing the image watermark which is preset to be superposed with the original image, and the second watermark image is a watermark image obtained by superposing the image watermark which is adjusted by the characteristic parameters with the original image;
the method includes the following steps that the test unit utilizes a preset intelligent image information removal attack test model to carry out intelligent watermark removal attack on each watermark image to be tested in the watermark image set to be tested to obtain an output image corresponding to each watermark image to be tested, and the method includes the following steps:
for any watermark image to be tested, respectively inputting the watermark image to be tested to N preset intelligent image information removal attack test models, and acquiring an output image after the intelligent watermark corresponding to the watermark image to be tested is removed; wherein N is more than or equal to 1;
the determining unit determines an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and the determining unit comprises:
determining an optimal watermark image according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, wherein the difference between the corresponding output image and the watermark image to be tested is the minimum, and the difference between the corresponding output image and the watermark image to be tested is smaller than a second threshold value, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark;
the testing unit determines that the difference between the watermark image to be tested and the corresponding output image is smaller than a second threshold value in the following way:
under the condition that the preset intelligent image information removal attack test model comprises a plurality of intelligent image information removal attack test models, for any watermark image to be tested, if the difference between the watermark image to be tested and each corresponding output image is smaller than a second threshold value, determining that the difference between the watermark image to be tested and the corresponding output image is smaller than the second threshold value; the method comprises the steps that different output images corresponding to a watermark image to be tested are subjected to intelligent watermark removal attack on the watermark image to be tested through different intelligent image information removal attack test models to obtain the watermark image to be tested;
the determining unit determines an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and the determining unit further comprises:
if the watermark images to be tested, which have the difference with the corresponding output images smaller than the second threshold value, do not exist, and the iteration times do not reach the maximum iteration times, generating a new adjusted image watermark set according to a preset image watermark optimization algorithm and the corresponding output images of the watermark images to be tested, and determining a new round of optimal watermark images and optimal image watermarks;
the determining unit determines an optimal watermark image and an optimal image watermark according to the difference between the watermark image to be tested and the output image corresponding to the watermark image to be tested, and the determining method further comprises the following steps:
if the watermark image to be tested with the difference between the watermark image to be tested and each corresponding output image smaller than the second threshold does not exist, and the iteration frequency reaches the maximum iteration frequency, determining the watermark image to be tested with the most intelligent image information removal attack test model attack failure as the optimal watermark image, and determining the image watermark superposed in the optimal watermark image as the optimal image watermark;
and if the difference between an output image obtained by removing the attack test model from the watermark image to be tested by using the intelligent image information removal attack test model and the watermark image to be tested is smaller than the second threshold value, determining that the watermark image to be tested makes the attack of the intelligent image information removal attack test model invalid.
12. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor, the processor being configured to execute the machine executable instructions to implement the method of any one of claims 1 to 9.
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