Data Processing Method, Apparatus, Electronic Device, and Storage Device

Abstract

The present application discloses a data processing method, including: obtaining a carrier object and target watermark information; embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information; obtaining the relationship between a parameter for characterizing the watermark hiding effect and a parameter for characterizing the watermark extraction effect according to the carrier object containing the target watermark information; when the relationship does not satisfy a preset relationship, updating the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the watermark extraction effect satisfies the preset relationship. By adopting the above method, the problem that the watermark cannot be effectively extracted when the image is geometrically attacked by a small-angle rotation existing in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and particularly relates to two data processing methods, apparatuses, electronic devices, and storage devices. Background Art

[0002] With the rapid development of the Internet, the speed of information dissemination far exceeds any previous period. As a common information carrier, digital images are widely used today in the digital information age. However, digital images are easy to spread, copy, and tamper with, and there are still problems such as difficult-to-define image copyright attribution, anti-counterfeiting, and anti-tampering in practical applications. For this reason, researchers have proposed digital image watermarking technology. Digital image watermarking technology embeds some identification information into digital images without affecting the use value of the original carrier. Through the information hidden in the images, purposes such as confirming the image creator, purchaser, transmitting secret information, or determining whether the image has been tampered with can be achieved. Digital image watermarking is an effective method for protecting the security of digital images, realizing anti-counterfeiting traceability, and copyright protection, and is an important branch and research direction in the field of information hiding technology research.

[0003] Images often suffer from geometric attacks during transmission, such as rotation, scaling, translation, collage, compression, etc. Under these geometric attacks, the watermarks embedded in digital images face the risk of failure. And due to the wide existence of this attack method, if the problems caused by this transmission method cannot be solved in time, it will bring great trouble to the copyright protection of images.

[0004] Existing digital image watermarking technology is only applicable to embedding and extracting watermarks when the image is geometrically attacked by a large-angle rotation. When the image is geometrically attacked by a small-angle rotation, the existing scheme cannot effectively extract the embedded watermark. Summary of the Invention

[0005] The present application provides a data processing method, apparatus, electronic device, and storage device to solve the problem that the watermark cannot be effectively extracted when the image is geometrically attacked by a small-angle rotation.

[0006] The present application provides a data processing method, including:

[0007] Obtain a carrier object and target watermark information;

[0008] Embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0009] According to the carrier object containing the target watermark information, obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect;

[0010] When the relationship does not satisfy the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect satisfies the preset relationship.

[0011] Optionally, embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information includes:

[0012] Performing a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object;

[0013] Embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength;

[0014] Performing an inverse transformation on the frequency domain coefficients embedded with the target watermark information to obtain a carrier object containing the target watermark information.

[0015] Optionally, performing a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object includes:

[0016] Performing a Fourier transform on the carrier object to obtain the Fourier coefficients of the carrier object;

[0017] Embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength includes:

[0018] Embedding the target watermark information into the amplitude values of the Fourier coefficients of the carrier object according to the current embedding strength.

[0019] Optionally, the parameter for characterizing the extracted watermark effect is obtained by the following method:

[0020] Calculating the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image;

[0021] Obtaining the parameter for characterizing the extracted watermark effect according to the detectable autocorrelation peak and the correlation peak template of the autocorrelation image.

[0022] Optionally, the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before embedding the watermark and the carrier object containing the target watermark information.

[0023] Optionally, the preset relationship is:

[0024] The value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect.

[0025] Optionally, updating the current watermark embedding strength includes:

[0026] Adding a preset step size to the current watermark embedding strength to obtain the updated current watermark embedding strength.

[0027] This application also provides a data processing method, including:

[0028] Obtaining a carrier object containing watermark information;

[0029] Obtaining a self-synchronized self-correlation image based on the self-correlation image and the phase correlation peak of the carrier object; the phase correlation peak is obtained based on the self-correlation image of the carrier object and a preset correlation peak template;

[0030] Obtaining the rotation angle and scaling factor of the carrier object based on the correlation between the self-synchronized self-correlation image and the preset correlation peak template;

[0031] Extracting the watermark information from the carrier object based on the rotation angle and scaling factor.

[0032] Optionally, obtaining the rotation angle and scaling factor of the carrier object based on the correlation between the self-synchronized self-correlation image and the correlation peak template includes:

[0033] Obtaining a target phase correlation peak based on the correlation between the self-synchronized self-correlation image and the preset correlation peak template; the target phase correlation peak refers to the phase correlation peak used to determine the rotation angle and scaling factor of the carrier object;

[0034] Obtaining the rotation angle and scaling factor of the carrier object based on the target phase correlation peak.

[0035] Optionally, obtaining the target phase correlation peak based on the correlation between the self-synchronized self-correlation image and the correlation peak template includes:

[0036] Judging whether the correlation between the self-synchronized self-correlation image and the correlation peak template meets a preset correlation relationship. If it meets, using the phase correlation peak corresponding to the self-synchronized self-correlation image as the target phase correlation peak.

[0037] Optionally, the correlation between the self-synchronized self-correlation image and the correlation peak template is: the weighted cross-correlation between the phase cross-correlation coefficient between the self-synchronized self-correlation image and the correlation peak template and the spatial cross-correlation coefficient between the self-synchronized self-correlation image and the correlation peak template.

[0038] Optionally, the preset correlation relationship is:

[0039] The difference between the weighted cross-correlation and a preset constant is less than a preset difference threshold.

[0040] Optionally, it further includes:

[0041] Performing a sharpening process on the self-correlation image after self-synchronization to obtain a sharpened self-correlation image after self-synchronization;

[0042] The obtaining of the target phase correlation peak according to the correlation between the self-correlation image after self-synchronization and the correlation peak template includes:

[0043] Obtaining the target phase correlation peak according to the correlation between the sharpened self-correlation image after self-synchronization and the correlation peak template.

[0044] Optionally, it further includes:

[0045] Obtaining the self-correlation image of the carrier object according to the carrier object.

[0046] Optionally, the obtaining of the self-correlation peak image of the carrier object according to the carrier object includes:

[0047] Performing a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain signal of the carrier object;

[0048] Performing a filtering process on the frequency domain signal of the carrier object to obtain the self-correlation image of the carrier object.

[0049] Optionally, it further includes:

[0050] Performing a noise removal process on the self-correlation image of the carrier object to obtain a self-correlation image after noise removal;

[0051] The obtaining of the phase correlation peak according to the self-correlation image of the carrier object and the correlation peak template includes:

[0052] Obtaining the phase correlation peak according to the self-correlation image after noise removal and the correlation peak template;

[0053] The obtaining of the self-correlation image after self-synchronization according to the self-correlation image of the carrier object and the phase correlation peak includes:

[0054] Obtaining the self-correlation image after self-synchronization according to the self-correlation image after noise removal and the phase correlation peak.

[0055] Optionally, the obtaining of the self-correlation image after self-synchronization according to the self-correlation image after noise removal and the phase correlation peak includes:

[0056] Calculate the rotation angle and scaling factor of the autocorrelation image of the carrier object according to the phase correlation peak;

[0057] Perform rotation and scaling processing on the autocorrelation image of the carrier object according to the rotation angle and scaling factor to obtain the autocorrelation image after self-synchronization.

[0058] This application also provides a data processing device, including:

[0059] An information acquisition unit, configured to acquire a carrier object and target watermark information;

[0060] A target watermark information embedding unit, configured to embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0061] A parameter relationship obtaining unit, configured to obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect according to the carrier object containing the target watermark information;

[0062] A current watermark embedding strength updating unit, configured to update the current watermark embedding strength when the relationship does not meet the preset relationship until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship.

[0063] This application also provides an electronic device, including:

[0064] A processor;

[0065] A memory, configured to store the program of the data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed:

[0066] Acquire a carrier object and target watermark information;

[0067] Embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0068] Obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect according to the carrier object containing the target watermark information;

[0069] When the relationship does not meet the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship.

[0070] This application also provides a storage device, storing the program of the data processing method. When the program is run by the processor, the following steps are executed:

[0071] Obtain a carrier object and target watermark information;

[0072] Embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0073] Obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect based on the carrier object containing the target watermark information;

[0074] When the relationship does not meet the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship.

[0075] This application also provides a data processing device, including:

[0076] A carrier object obtaining unit for obtaining a carrier object containing watermark information;

[0077] A self - synchronized autocorrelation image obtaining unit for obtaining a self - synchronized autocorrelation image according to the autocorrelation image and the phase - correlation peak of the carrier object; the phase - correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template;

[0078] A rotation angle and scaling factor obtaining unit for obtaining the rotation angle and scaling factor of the carrier object according to the correlation between the self - synchronized autocorrelation image and the correlation peak template;

[0079] A watermark information extraction unit for extracting watermark information from the carrier object according to the rotation angle and scaling factor.

[0080] This application also provides an electronic device, including:

[0081] A processor;

[0082] A memory for storing a program of the data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed:

[0083] Obtain a carrier object containing watermark information;

[0084] Obtain a self - synchronized autocorrelation image according to the autocorrelation image and the phase - correlation peak of the carrier object; the phase - correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template;

[0085] Obtain the rotation angle and scaling factor of the carrier object according to the correlation between the self-synchronized autocorrelation image and the preset correlation peak template;

[0086] Extract the watermark information from the carrier object according to the rotation angle and scaling factor.

[0087] This application also provides a storage device storing a program for a data processing method, and the program is run by a processor to execute the following steps:

[0088] Obtain a carrier object containing watermark information;

[0089] Obtain the self-synchronized autocorrelation image according to the autocorrelation image and phase correlation peak of the carrier object; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template;

[0090] Obtain the rotation angle and scaling factor of the carrier object according to the correlation between the self-synchronized autocorrelation image and the preset correlation peak template;

[0091] Extract the watermark information from the carrier object according to the rotation angle and scaling factor.

[0092] Compared with the prior art, this application has the following advantages:

[0093] This application provides a data processing method. After embedding the target watermark information into the carrier object according to the current watermark embedding strength, this application obtains the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect; when updating the current watermark embedding strength when the relationship does not satisfy the preset relationship, the target watermark information is embedded into the carrier object again using the new current watermark embedding strength to obtain a carrier object containing the target watermark information, and the relationship between the two parameters is calculated again until the preset relationship is satisfied to complete the embedding of the target watermark information. Since a certain balance is achieved between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect after watermark embedding, the watermark can be effectively extracted even under geometric attacks with a small-angle rotation of the carrier object.

[0094] The present application also provides a data processing method. According to the autocorrelation image and the phase correlation peak of the carrier object, an autocorrelation image after self-synchronization is obtained; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template; according to the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template, the rotation angle and the scaling factor of the carrier object are obtained; according to the rotation angle and the scaling factor, watermark information is extracted from the carrier object. When obtaining the rotation angle and the scaling factor of the carrier object by the above method, since the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template is considered, accurate rotation angle and scaling factor can be obtained, and thus the watermark information can be effectively extracted.

[0095] In a preferred solution, according to the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template, a target phase correlation peak is obtained; the target phase correlation peak refers to the phase correlation peak used to determine the rotation angle and the scaling factor of the carrier object; according to the target phase correlation peak, the rotation angle and the scaling factor of the carrier object are obtained. In the above solution, the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template is considered when obtaining the target phase correlation peak, so the obtained target phase correlation peak can accurately reflect the rotation angle and the scaling factor of the carrier object. Description of the Drawings

[0096] Figure 1 is a flowchart of a data processing method provided in the first embodiment of the present application.

[0097] Figure 2 is a schematic diagram of a watermark tiling method provided in the first embodiment of the present application.

[0098] Figure 3 is a flowchart of a data processing method provided in the second embodiment of the present application.

[0099] Figure 4 is a schematic diagram of a phase correlation peak provided in the second embodiment of the present application.

[0100] Figure 5 is a schematic diagram of a convex hull mask of a self-correlation peak image after synchronization and a preset correlation peak template provided in the second embodiment of the present application.

[0101] Figure 6 is a schematic diagram of a data processing device provided in the third embodiment of the present application.

[0102] Figure 7 A schematic diagram of an electronic device provided in the fourth embodiment of the present application.

[0103] Figure 8 is a schematic diagram of a data processing device provided in the sixth embodiment of the present application. Detailed implementation manners

[0104] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific implementations disclosed below.

[0105] The first embodiment of the present application provides a data processing method, which is applied to a watermark embedding end. The following will be described in conjunction with Figure 1 、 Figure 2 for illustration.

[0106] As Figure 1 shown, in step S101, a carrier object and target watermark information are obtained.

[0107] The carrier object refers to a carrier image or a carrier video prepared to embed the target watermark information. Among them, the carrier image can be a dynamic image or a static image. For example, the image can be a dynamic image in GIF (Graphics Interchange Format) format, or it can also be a static image in JPEG (Joint Photographic Experts Group) format. For example, when the copyright owner of a certain image needs to distribute the content to multiple partners, different watermarks need to be embedded so that when piracy occurs, it can be traced from which partner it leaked. This image is a carrier object. In addition, the carrier video can be a physical video file. For example, the carrier video is a video file stored in a remote server for local download and playback; it can also be in the form of streaming media. For example, the carrier video is a video stream provided by an online video on demand platform or an online live broadcast platform that can be directly streamed; in addition, the carrier video can be a video in the form of AR, VR, etc., or a stereoscopic video. Of course, with the continuous progress of technology, the carrier video can also be a video in other formats and other forms related to video, and no special limitation is made here.

[0108] The target watermark information refers to additional information added to the carrier object. The target watermark information can be a bit sequence of a predetermined number of bits. For example, adding copyright information as a watermark in the carrier object can prevent piracy.

[0109] As Figure 1 shown, in step S102, the target watermark information is embedded into the carrier object according to the current watermark embedding strength, and a carrier object containing the target watermark information is obtained.

[0110] Embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information includes:

[0111] Performing a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object;

[0112] According to the current watermark embedding strength, embedding the target watermark information into the frequency domain coefficients of the carrier object;

[0113] Performing an inverse transformation on the frequency domain coefficients embedded with the target watermark information to obtain a carrier object containing the target watermark information.

[0114] The performing a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object includes:

[0115] Performing a Fourier transform on the carrier object to obtain the Fourier coefficients of the carrier object;

[0116] The embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength includes:

[0117] Embedding the target watermark information into the amplitude values of the Fourier coefficients of the carrier object according to the current embedding strength.

[0118] The following introduces a specific process of embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information in combination with an example:

[0119] First, perform a discrete Fourier transform on the carrier object, generate a watermark signal according to the target watermark information, modulate the watermark signal to form a rectangular watermark block with a size of 128x100, and embed the rectangular watermark block in a tiled manner within an 8x8 area symmetric about the frequency domain center, as Figure 2 shown. The watermark signal can be embedded into the amplitude values of the Fourier coefficients according to formula (1).

[0120] Re{F′(u,v)} = Re{F(u,v)} + α(u,v)W(u,v) (1)

[0121] Among them, Re{F(u,v)} represents the Fourier amplitude spectrum of the carrier video / image before embedding the watermark signal, Re{F′(u,v)} represents the Fourier amplitude spectrum of the carrier video / image after embedding the watermark signal, W(u,v) is the watermark signal, and α(u, v) represents the watermark embedding strength. After completing the watermark embedding, combined with the phase of the Fourier transform, an inverse transform is performed to obtain the carrier video / image containing the watermark.

[0122] AsFigure 1 As shown, in step S103, based on the carrier object containing the target watermark information, the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect is obtained.

[0123] The parameter for characterizing the extracted watermark effect can be obtained through the following method:

[0124] Calculate the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image;

[0125] Based on the detectable autocorrelation peak and the correlation peak template of the autocorrelation image, obtain the parameter for characterizing the extracted watermark effect.

[0126] The parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information.

[0127] In the first embodiment of the present application, the parameter for characterizing the watermark hiding effect can be the normalized correlation coefficient NCA between the autocorrelation image and the correlation peak template, and the parameter for characterizing the watermark hiding effect can be the structural similarity SSIM between the carrier object before watermark embedding and the carrier object containing the target watermark information at the current watermark embedding strength. Among them, the definition of NCA is as follows:

[0128]

[0129] Where A(i, j) is the autocorrelation image of the image after embedding the self-synchronous watermark, and A T (i, j) is the correlation peak template. The closer the normalized cross-correlation coefficient between the two is to 1, the stronger the correlation between the two. Therefore, NCA(α) can measure the similarity between the extractable correlation peak and the correlation peak template when the current watermark embedding strength is α. SSIM is an index for measuring the similarity between two images. The SSIM index defines the image structure information as independent of brightness and contrast, and can reflect the attributes of the objects in the scene. SSIM is a combination of three different factors: brightness, contrast, and structure. Among them, the brightness metric is the mean value, the contrast metric is the standard deviation, and the structural similarity degree metric is the covariance. As the embedding strength increases, the SSIM index of the watermarked image decreases, while the detectable autocorrelation peak signal increases, making NCA(α) increase. When the NCA(α) coefficient first exceeds the SSIM coefficient, the current watermark embedding strength α at this time is used as the optimal embedding strength.

[0130] Such as Figure 1As shown, in step S104, when the relationship does not satisfy the preset relationship, update the current watermark embedding strength until the relationship between the parameter representing the watermark hiding effect and the parameter representing the extracted watermark effect satisfies the preset relationship.

[0131] The preset relationship is that the value of the parameter representing the extracted watermark effect is greater than the value of the parameter representing the watermark hiding effect.

[0132] The update of the current watermark embedding strength includes:

[0133] Add the preset step size to the current watermark embedding strength as the updated current watermark embedding strength.

[0134] The calculation of the current watermark embedding strength is as shown in Algorithm 1. The current watermark embedding strength traverses within the interval [iniA, iniA + wA] with a certain step size sA. Its initial value iniA can be determined by a linear regression model of the variance of the Fourier amplitude spectrum of the watermark embedding signal region, and the model is obtained by fitting the image training set. During the traversal, the strength is increased by sA each time. For the new embedding strength, calculate the new NCA(α) coefficient and SSIM coefficient. If the NCA(α) coefficient exceeds the SSIM coefficient for the first time, the watermark embedding strength α at this time is used as the optimal embedding strength. Otherwise, continue the next loop and increase the current watermark embedding strength by sA again.

[0135] Algorithm 1 Algorithm for calculating the current watermark embedding strength

[0136] Input: Original carrier object (I), initial value of embedding strength (iniA), strength step size (sA), strength traversal window width (wA);

[0137] Output: Optimal watermark embedding strength (optA), carrier object after embedding the watermark (Iw);

[0138] 1 for alpha = iniA: sA: (iniA + wA) number;

[0139] 2 Embed the watermark into the original carrier object I with strength alpha according to formula (1) to obtain the carrier object after embedding the watermark (Iw);

[0140] 3 Calculate the autocorrelation of the carrier object after embedding the watermark at the current embedding strength to obtain the autocorrelation image A;

[0141] 4 Calculate the NCA coefficient according to formula (2);

[0142] 5 Calculate the SSIM coefficient of Iw at this time;

[0143] 6 if NCA(alpha) > SSIM

[0144] 7 If optA = alpha, then exit the loop;

[0145] 8 End if

[0146] 9 End

[0147] 10 Output optA and Iw;

[0148] Corresponding to a data processing method provided in the first embodiment of the present application, a second embodiment of the present application provides another data processing method, which is applied to a watermark extraction end. The following will be described in conjunction with Figure 3 , Figure 4 , Figure 5 for introduction.

[0149] As Figure 3 shown, in step S301, a carrier object containing watermark information is obtained.

[0150] The carrier object refers to a carrier image or carrier video into which the target watermark information has been embedded.

[0151] As Figure 3 shown, in step S302, according to the autocorrelation image and the phase correlation peak of the carrier object, an autocorrelation image after self-synchronization is obtained; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template.

[0152] When the size and tiling method of the embedded rectangular watermark template are fixed, the position of the template autocorrelation peak is a determined value. The preset correlation peak template refers to an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak. The second embodiment of the present application may further include:

[0153] According to the carrier object, obtain the autocorrelation image of the carrier object.

[0154] The obtaining the autocorrelation image of the carrier object according to the carrier object includes:

[0155] Perform a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain signal of the carrier object;

[0156] Perform filtering processing on the frequency domain signal of the carrier object and calculate the autocorrelation of the filtered carrier object to obtain the autocorrelation image of the carrier object.

[0157] The second embodiment of the present application may further include:

[0158] Perform noise removal processing on the autocorrelation image of the carrier object to obtain an autocorrelation image after noise removal;

[0159] Obtaining a phase correlation peak based on the autocorrelation image of the carrier object and the correlation peak template includes:

[0160] Obtaining a phase correlation peak based on the denoised autocorrelation image and the correlation peak template;

[0161] Obtaining the post-self-synchronization autocorrelation image based on the autocorrelation image of the carrier object and the phase correlation peak includes:

[0162] Obtaining the post-self-synchronization autocorrelation image based on the denoised autocorrelation image and the phase correlation peak.

[0163] In specific implementation, first perform a discrete Fourier transform on the carrier object to obtain a frequency-domain signal, then use a high-pass filter in the pre-stage of the frequency-domain information to remove the interference of the carrier object on the watermark signal, calculate the autocorrelation image of the filtered carrier image, and then perform a high-pass filter on the autocorrelation image to obtain the autocorrelation peak of the carrier image. In order to remove the high-correlation peak noise in the central region, perform an operation to remove the central region; then, perform a log-polar coordinate transform on the denoised autocorrelation image and the preset correlation peak template respectively, and then perform phase matching on the transformed autocorrelation image and the preset correlation peak template to obtain a POMF (phase only matched filtering) phase correlation peak. In the log-polar coordinate domain, the rotation and scaling attacks on the image are manifested as displacements along the coordinate axes, and the specific relationship is shown in formula (3).

[0164]

[0165] Where (ρ′, θ′) is the new coordinate in the log-polar coordinate system after the original coordinate (ρ, θ) is attacked by the scaling and rotation parameters σ and α. It can be seen from formula (3) that the scale transformation of the video / image will cause the image to have a displacement of lnσ on the ρ initial position (original scale) in the log-polar coordinate system, and the rotation of the image will cause the image to have an angular offset of α on the basis of the initial angle of the angular axis θ in the log-polar coordinate system.

[0166] When performing autocorrelation operation on the carrier object, multiple autocorrelation peaks will be formed in the area around the central highest peak. These peaks belong to noise and will affect the result of subsequent phase-matching filtering, and it is easy to form multiple incorrect POMF phase-correlation peaks. Therefore, during the detection process, in order to reduce the influence of this noise on the true watermark autocorrelation peak, the central area should be removed. In the embodiment of the present application, the correlation peak noise within the 32x25 rectangular area around the center of the autocorrelation domain in the Fourier domain of the watermark image is removed, and the values in this area are set to 0. The selection of the area size is related to the size of the embedded rectangular watermark template. The template size used in the embodiment of the present application is 128x100, and the position of the correlation peak is at the position (64, 50) away from the central area. The rotation angle mainly considered in the embodiment of the present application is 0 to 5 degrees, and the scaling factor is 0.5 to 1.5. Therefore, after rotation and scaling, the position of the correlation peak is outside the range of (32, 25). Therefore, in this article, the correlation peak noise within the 32x25 rectangular area of the central area is removed, and only the autocorrelation peak at the center point position is retained.

[0167] The obtaining of the self-synchronized autocorrelation image according to the autocorrelation image after noise removal and the phase-correlation peak includes:

[0168] Calculating the rotation angle and scaling factor of the autocorrelation image of the carrier object according to the phase-correlation peak;

[0169] Performing rotation and scaling processing on the autocorrelation image of the carrier object according to the rotation angle and scaling factor to obtain the self-synchronized autocorrelation image.

[0170] As Figure 3 shown, in step S303, according to the correlation between the self-synchronized autocorrelation image and the correlation peak template, the rotation angle and scaling factor of the carrier object are obtained.

[0171] The obtaining of the rotation angle and scaling factor of the carrier object according to the correlation between the self-synchronized autocorrelation image and the correlation peak template includes:

[0172] Obtaining the target phase-correlation peak according to the correlation between the self-synchronized autocorrelation image and the correlation peak template; the target phase-correlation peak refers to the phase-correlation peak used to determine the rotation angle and scaling factor of the carrier object;

[0173] Obtaining the rotation angle and scaling factor of the carrier object according to the target phase-correlation peak.

[0174] The obtaining of the target phase-correlation peak according to the correlation between the self-synchronized autocorrelation image and the correlation peak template includes:

[0175] Determine whether the correlation between the self-synchronized autocorrelation image and a preset correlation peak template meets a preset correlation relationship. If it meets, use the phase correlation peak corresponding to the self-synchronized autocorrelation image as the target phase correlation peak.

[0176] The correlation between the self-synchronized autocorrelation image and the correlation peak template is: the weighted cross-correlation between the phase cross-correlation coefficient between the self-synchronized autocorrelation image and the correlation peak template and the spatial cross-correlation coefficient between the self-synchronized autocorrelation image and the correlation peak template.

[0177] The preset correlation relationship is:

[0178] The difference between the weighted cross-correlation and a preset constant is less than a preset difference threshold.

[0179] The following introduces the process of obtaining the target phase correlation peak according to the correlation between the self-synchronized autocorrelation image and a preset correlation peak template.

[0180] In video / image watermarking applications, in most cases, it is difficult to obtain the original image or reference image at the watermark extraction end. Therefore, only the autocorrelation peaks formed by repeatedly embedded watermarks that can be detected can be obtained and phase-matched with a preset correlation peak template. The intensity of these watermark signals is relatively weak, and the number of autocorrelation peaks that can be detected by general automated methods is often insufficient. Therefore, it is easy to form phase correlations with the preset correlation peak template under various rotation and scaling parameters, resulting in interference as Figure 4 shown, and it is difficult to be determined by the highest POMF phase correlation peak. Therefore, the present application proposes an accurate method for screening POMF target phase correlation peaks with the cross-correlation coefficient between the self-synchronized correlation peak and a preset correlation peak template as the determination condition. The pseudo-code of the algorithm can be as follows:

[0181] Input: Sorted phase correlation peaks (SPC), autocorrelation image of the carrier object (Wa), preset correlation peak template (Ha)

[0182] Output: Rotation angle θ, scaling parameter s

[0183] 1. for i < number of SPC

[0184] 2. Calculate θ and s according to SPC(i);

[0185] 3. Perform rotation and scaling transformation on Wa with θ and s to obtain the self-synchronized Wa';

[0186] 4. Sharpen the peaks in the resynchronized Wa' using the local maximum method;

[0187] 5. Calculate the phase cross-correlation coefficient PCorr between the templates of Ha and Wa' according to formula (4);

[0188] 6. Calculate the spatial cross-correlation coefficient SCorr between the templates of Ha and Wa' according to formula (5);

[0189] 7. Calculate the weighted cross-correlation Corr according to formula (6);

[0190] 8. If Corr is close to 1, select this SPC(i) as the correct POMF peak;

[0191] 9.end

[0192] 10. Calculate θ and s according to SPC;

[0193] In the second embodiment of the present application, the peak value of POMF is automatically screened by calculating the cross-correlation coefficient between the self-correlation peak image Wa' after self-synchronization and the preset correlation peak template Ha. The cross-correlation coefficient includes the weighting of both phase cross-correlation and spatial cross-correlation, thus considering the effects of both rotation and scaling attacks simultaneously. First, in terms of phase cross-correlation, the method of Fourier-Mellin phase cross-correlation is mainly adopted. As shown in formula (4), the correlation between the synchronized correlation peak image and the preset correlation peak template (Ha) is calculated. The specific method is to first sort the phase correlations output from the previous POMF step from large to small, start traversing from the highest peak, and calculate the rotation angle and scaling coefficient according to its position in the log-polar coordinate system (Log-polar). Then, rotate and scale the watermark self-correlation image Wa back to the state synchronized with the original carrier image according to the rotation angle and scaling coefficient to obtain Wa' after synchronization. Then, calculate the phase cross-correlation between the self-correlation peak image Wa' after synchronization and the preset correlation peak template Ha again according to formula (4):

[0194]

[0195] Next, calculate the spatial cross-correlation between the self-correlation peak image Wa' after synchronization and the preset correlation peak template Ha. The spatial cross-correlation is defined by the cross-correlation between the figures enclosed by the convex hulls formed by the positions of the correlation peaks on the self-synchronized Wa' and the template Ha. The convex hull masks of Wa' and Ha are as shown in Figure 5 (a) and (b), where Figure 5 (b) is the binary image enclosed by the convex hull formed by Wa' after self-synchronization, Figure 5 (a) is the binary image enclosed by the convex hull of Ha. These two figures can more accurately reflect the correlation between the original image and the image after geometric attack, and their correlation can be calculated by the normalized correlation coefficient defined by formula (2), and the specific definition is as shown in formula (5),

[0196]

[0197] The specific process of the convex hull generation algorithm is as follows: First, perform two-dimensional triangulation on the points in the space of Wa', and the Delaunay triangulation algorithm is adopted. Next, search for its convex hull using the Graham scan method according to the triangulation result. Then, fill the convex hull to obtain the binary image B. w Finally, the binary image B of the convex hull of Wa' w and the binary image B of the convex hull of Ha H are substituted into formula (5) to obtain the spatial correlation coefficient NCB. In order to comprehensively consider the correlation coefficients of phase correlation and spatial correlation between the post-self-synchronization image and the original image, this method calculates the weighted correlation coefficient as shown in formula (6).

[0198] <![CDATA[Corr = wP corr +(1 - w)S corr > (6)

[0199] Among them, w is the weight, w takes the value of 0.5, and Corr is the weighted cross-correlation coefficient. When the cross-correlation coefficient Corr of formula (5) is closer to 1, it indicates that the correlation between the post-self-synchronization image and the autocorrelation template is stronger. At this time, the rotation angle corresponding to the phase cross-correlation peak SPC is the correct rotation angle.

[0200] As an implementation manner, the second embodiment of the present application may further include: performing sharpening processing on the post-self-synchronization autocorrelation image to obtain the sharpened post-self-synchronization autocorrelation image.

[0201] Obtaining the target phase correlation peak according to the correlation between the post-self-synchronization autocorrelation image and the correlation peak template includes:

[0202] Obtaining the target phase correlation peak according to the correlation between the sharpened post-self-synchronization autocorrelation image and the correlation peak template.

[0203] The second embodiment of the present application proposes to perform sharpening processing on the post-self-synchronization autocorrelation image, which can effectively improve the calculation accuracy of the cross-correlation coefficient between it and the preset correlation peak template. Before calculating the cross-correlation coefficient between the watermark autocorrelation peak and the preset correlation peak template, it is necessary to perform re-synchronization operations on the watermark autocorrelation peak. When re-synchronizing, it is necessary to rotate and scale the watermark autocorrelation image. The rotation and scaling include linear interpolation operations, and the interpolation will cause high signals to be generated around the peak, affecting the accurate position of the peak. Therefore, it is very necessary to sharpen and optimize the post-self-synchronization autocorrelation peak. The specific sharpening method is to use a 5x5 sliding window to calculate the local maximum value within the local window for the image Wa' re-synchronized according to the rotation angle and scaling coefficient determined by the current phase correlation peak. The pseudo-code of the sharpening algorithm is as follows:

[0204] Input: a sliding window of size 5x5, a threshold of 0.1 (empirical value), and the autocorrelation peak image Wa of the carrier object

[0205] Output: 9 local maxima in the watermark autocorrelation peak image and the sharpened self-synchronization correlation peak image Wa′

[0206] 1. Find the local maximum in the local window lw(i) and set lw(i) to 0;

[0207] 2. Traverse Wa with the sliding window to find all local maxima;

[0208] 3. Sort all the local maxima found in step 2 and retain the 9 largest ones;

[0209] 4. Generate the sharpened Wa′ with 9 maximum peaks and the rest set to 0

[0210] It should be noted that the above sharpening algorithm is only one implementation. In specific implementation, other numbers of maximum peaks can also be selected.

[0211] As Figure 3 shown, in step S304, the watermark information is extracted from the carrier object according to the rotation angle and the scaling factor.

[0212] The local region maximum value algorithm searches for its local maxima in the input matrix Wa. The specific method is to compare the maximum value in the matrix Wa with the threshold specified by the user to achieve the search for local maxima. When the maximum value is greater than or equal to the specified threshold, its value is regarded as a valid local maximum. The determination of local maxima is based on a local sliding window. After finding a local maximum, all matrix values (including the maximum value) in the sliding window are set to 0. This step ensures that this maximum value is not included in subsequent searches. The size of the local sliding window must be appropriate so that enough values around the maximum value can be eliminated, thereby reducing false peaks. The sliding window repeats this process until all valid maxima are found. Finally, the required number of the highest values among the local maxima are retained.

[0213] In practical applications, most of the geometric attacks on videos / images are attacks at small rotation angles, with the rotation angle ranging from 1° to 5°. However, at small rotation angles, it is difficult to detect the rotation and scaling parameters of geometric attacks because, different from large-angle rotations, after small-angle rotations, the periodicity brought by linear interpolation to videos / images is very weak. If the watermark template embedded by periodicity is used for detection, there is also the problem of weak signals, and it is difficult to balance robustness and invisibility. Specifically, the highest phase correlation peak in the log-polar coordinate system does not necessarily correspond to the accurate rotation and scaling coefficients of geometric attacks. Because in many videos / images, the texture content itself contains very strong periodicity, and this periodicity has a great impact on the detection of geometric attack parameters. Therefore, it is very necessary to find the peak corresponding to the accurate geometric attack parameters in the phase correlation peak, which helps to improve the robustness and invisibility of the watermark algorithm. The embodiment of this application proposes a new self-synchronizing watermark algorithm in the log-polar coordinate domain that combines an optimized adaptive watermark embedding strength algorithm, an accurate POMF target phase correlation peak screening method with the cross-correlation coefficient of the autocorrelation peak as the judgment condition, and an autocorrelation peak sharpening algorithm, improving the performance of the self-synchronizing watermark algorithm.

[0214] To illustrate the effectiveness of this application, the performance of the log-polar coordinate domain POMF rotation and scaling detection with and without the method proposed in the embodiment of this application was tested in the experiment. A total of 8 images with various resolutions were used as the test pictures. The angle detection accuracy under the attack with a rotation angle of 1° to 5° (step size of 1°) was tested, and at the same time, the rotation angle and scaling coefficient detection accuracy under the mixed attack when the scaling coefficient was 0.7 to 1.3 (step size of 0.1) were tested. Under the above experimental conditions, this test included both separate rotation or scaling geometric attacks and the performance of this algorithm under the combined rotation and scaling mixed geometric attack. Table 1 shows the test results of the relatively typical Image 1.

[0215] According to Table 1, if the method proposed in the embodiment of the present application is adopted, the average rotation angle error detected by the POMF filter in the log-polar coordinate domain is 5°. If the method proposed in this article is not used, the angle error is 6°. The scaling error obtained by using this method is 0.0328, and the scaling error without using this method is 0.0403. In some cases, the angle error is large, resulting in a relatively large average angle error. If the preset detected angle error within 1° is acceptable, for the method of the embodiment of the present application, there are 8 cases where the detected rotation angle error is greater than 1°. If this method is not adopted, there are 10 cases where the detected rotation angle error is greater than 1°. Table 2 gives the average value of the test results of 8-spoke images. From the comparison of the above results, it can be seen that due to the effectiveness of this method, accurate geometric attack parameters can be found among the phase correlation peaks in multiple log-polar coordinate domains. If this method is not adopted and only judged by the highest phase correlation peak, a large geometric parameter error will be generated. Therefore, this experiment demonstrates the effectiveness of the method proposed in the embodiment of the present application.

[0216] Table 1 Detection Results of Geometric Attack Parameters

[0217]

[0218]

[0219] Table 2 Average Values of Test Results of Images with Different Resolutions

[0220]

[0221]

[0222] Corresponding to a data processing method provided in the first embodiment of the present application, a third embodiment of the present application provides an image processing apparatus.

[0223] As Figure 6 shown, the data processing apparatus includes:

[0224] An information acquisition unit 601, configured to acquire a carrier object and target watermark information;

[0225] A target watermark information embedding unit 602, configured to embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0226] A parameter relationship obtaining unit 603, configured to obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect according to the carrier object containing the target watermark information;

[0227] A current watermark embedding strength updating unit 604, configured to update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect satisfies a preset relationship when the relationship does not satisfy the preset relationship.

[0228] Optionally, the target watermark information embedding unit is specifically configured to:

[0229] Perform a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object;

[0230] Embed the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength;

[0231] Perform an inverse transformation on the frequency domain coefficients embedded with the target watermark information to obtain a carrier object containing the target watermark information.

[0232] Optionally, the target watermark information embedding unit is specifically configured to:

[0233] Perform a Fourier transform on the carrier object to obtain the Fourier coefficients of the carrier object;

[0234] The step of embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength includes:

[0235] Embed the target watermark information into the amplitude values of the Fourier coefficients of the carrier object according to the current embedding strength.

[0236] Optionally, the parameter for characterizing the extracted watermark effect is obtained by the following method:

[0237] Calculate the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image;

[0238] Obtain the parameter for characterizing the extracted watermark effect according to the detectable autocorrelation peaks and the correlation peak template of the autocorrelation image.

[0239] Optionally, the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information.

[0240] Optionally, the preset relationship is:

[0241] The value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect.

[0242] Optionally, the current watermark embedding strength updating unit is specifically configured to:

[0243] Add the preset step size to the current watermark embedding strength as the updated current watermark embedding strength.

[0244] It should be noted that for the detailed description of the device provided in the third embodiment of the present application, reference can be made to the relevant description of the first embodiment of the present application, which will not be elaborated here.

[0245] Corresponding to a data processing method provided in the first embodiment of the present application, a fourth embodiment of the present application provides an electronic device.

[0246] As Figure 7 shown, the electronic device includes:

[0247] A processor 701;

[0248] A memory 702 for storing a program of the data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed:

[0249] Obtain a carrier object and target watermark information;

[0250] Embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information;

[0251] According to the carrier object containing the target watermark information, obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect;

[0252] When the relationship does not meet the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship.

[0253] Optionally, the embedding of the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information includes:

[0254] Perform a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object;

[0255] According to the current watermark embedding strength, embed the target watermark information into the frequency domain coefficients of the carrier object;

[0256] Perform an inverse transformation on the frequency domain coefficients embedded with the target watermark information to obtain a carrier object containing the target watermark information.

[0257] Optionally, the performing a transformation on the carrier object from the spatial domain to the frequency domain to obtain the frequency domain coefficients of the carrier object includes:

[0258] Perform a Fourier transform on the carrier object to obtain the Fourier coefficients of the carrier object;

[0259] Embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength includes:

[0260] According to the current embedding strength, embed the target watermark information into the amplitude values of the Fourier coefficients of the carrier object.

[0261] Optionally, the parameter for characterizing the extracted watermark effect is obtained by the following method:

[0262] Calculate the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image;

[0263] Obtain the parameter for characterizing the extracted watermark effect according to the detectable autocorrelation peak and the correlation peak template of the autocorrelation image.

[0264] Optionally, the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before embedding the watermark and the carrier object containing the target watermark information.

[0265] Optionally, the preset relationship is:

[0266] The value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect.

[0267] Optionally, updating the current watermark embedding strength includes:

[0268] Add a preset step size to the current watermark embedding strength as the updated current watermark embedding strength.

[0269] It should be noted that for the detailed description of the electronic device provided in the fourth embodiment of this application, reference can be made to the relevant description of the first embodiment of this application, which will not be elaborated here.

[0270] Corresponding to the data processing method provided in the first embodiment of this application, the fifth embodiment of this application provides a storage device storing a program of the data processing method, and the program is run by a processor to execute the following steps:

[0271] Obtain a carrier object and target watermark information;

[0272] According to the current watermark embedding strength, embed the target watermark information into the carrier object to obtain a carrier object containing the target watermark information;

[0273] Based on the carrier object containing the target watermark information, obtain the relationship between the parameter for characterizing the watermark embedding effect and the parameter for characterizing the extracted watermark effect;

[0274] When the relationship does not meet the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark embedding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship.

[0275] It should be noted that for the detailed description of the storage device provided in the fifth embodiment of the present application, reference can be made to the relevant description of the first embodiment of the present application, which will not be elaborated here.

[0276] Corresponding to the data processing method provided in the second embodiment of the present application, the sixth embodiment of the present application provides a data processing device.

[0277] As Figure 8 shown, the data processing device includes:

[0278] A carrier object obtaining unit 801, configured to obtain a carrier object containing watermark information;

[0279] A self-synchronized autocorrelation image obtaining unit 802, configured to obtain a self-synchronized autocorrelation image according to the autocorrelation image of the carrier object and the phase correlation peak; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template;

[0280] A rotation angle and scaling coefficient obtaining unit 803, configured to obtain the rotation angle and scaling coefficient of the carrier object according to the correlation between the self-synchronized autocorrelation image and the correlation peak template;

[0281] A watermark information extraction unit 804, configured to extract watermark information from the carrier object according to the rotation angle and scaling coefficient.

[0282] Optionally, the rotation angle and scaling coefficient obtaining unit is specifically configured to:

[0283] Obtain a target phase correlation peak according to the correlation between the self-synchronized autocorrelation image and the correlation peak template; the target phase correlation peak refers to the phase correlation peak used to determine the rotation angle and scaling coefficient of the carrier object;

[0284] Obtain the rotation angle and scaling coefficient of the carrier object according to the target phase correlation peak.

[0285] Optionally, the rotation angle and scaling coefficient obtaining unit is specifically configured to:

[0286] Determine whether the correlation between the self-synchronized autocorrelation image and the preset correlation peak template satisfies a preset correlation relationship. If it is satisfied, use the phase correlation peak corresponding to the self-synchronized autocorrelation image as the target phase correlation peak.

[0287] Optionally, the correlation between the self-synchronized autocorrelation image and the preset correlation peak template is: the weighted cross-correlation between the phase cross-correlation coefficient between the self-synchronized autocorrelation image and the correlation peak template and the spatial cross-correlation coefficient between the self-synchronized autocorrelation image and the correlation peak template.

[0288] Optionally, the preset correlation relationship is:

[0289] The difference between the weighted cross-correlation and a preset constant is less than a preset difference threshold.

[0290] Optionally, the device further includes:

[0291] A sharpening processing unit, configured to perform sharpening processing on the self-synchronized autocorrelation image to obtain a sharpened self-synchronized autocorrelation image;

[0292] The step of obtaining the target phase correlation peak according to the correlation between the self-synchronized autocorrelation image and the correlation peak template includes:

[0293] Obtain the target phase correlation peak according to the correlation between the sharpened self-synchronized autocorrelation image and the correlation peak template.

[0294] Optionally, the device further includes: an autocorrelation image obtaining unit, configured to obtain the autocorrelation image of the carrier object according to the carrier object.

[0295] Optionally, the autocorrelation image obtaining unit is specifically configured to:

[0296] Perform a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain signal of the carrier object;

[0297] Perform filtering processing on the frequency domain signal of the carrier object to obtain the autocorrelation image of the carrier object.

[0298] Optionally, the device further includes: an autocorrelation image denoising unit, configured to perform noise removal processing on the autocorrelation image of the carrier object to obtain a denoised autocorrelation image;

[0299] The step of obtaining the phase correlation peak according to the autocorrelation image of the carrier object and the correlation peak template includes:

[0300] Obtain the phase correlation peak according to the denoised autocorrelation image and the correlation peak template;

[0301] The self - synchronization post - autocorrelation image obtaining unit is specifically configured to:

[0302] Obtain the self - synchronization post - autocorrelation image according to the denoised autocorrelation image and the phase - correlation peak.

[0303] Optionally, the self - synchronization post - autocorrelation image obtaining unit is specifically configured to:

[0304] Calculate the rotation angle and the scaling factor of the autocorrelation image of the carrier object according to the phase - correlation peak;

[0305] Perform rotation and scaling processing on the autocorrelation image of the carrier object according to the rotation angle and the scaling factor to obtain the self - synchronization post - autocorrelation image.

[0306] It should be noted that for the detailed description of the device provided in the sixth embodiment of the present application, reference can be made to the relevant description of the second embodiment of the present application, which will not be elaborated here.

[0307] Corresponding to the data processing method provided in the second embodiment of the present application, the seventh embodiment of the present application provides an electronic device, including:

[0308] A processor;

[0309] A memory for storing the program of the data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed:

[0310] Obtain a carrier object containing watermark information;

[0311] Obtain the self - synchronization post - autocorrelation image according to the autocorrelation image of the carrier object and the phase - correlation peak; the phase - correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation - peak template;

[0312] Obtain the rotation angle and the scaling factor of the carrier object according to the correlation between the self - synchronization post - autocorrelation image and the correlation - peak template;

[0313] Extract the watermark information from the carrier object according to the rotation angle and the scaling factor.

[0314] Optionally, the obtaining the rotation angle and the scaling factor of the carrier object according to the correlation between the self - synchronization post - autocorrelation image and the preset correlation - peak template includes:

[0315] Obtain a target phase - correlation peak according to the correlation between the self - synchronization post - autocorrelation image and the correlation - peak template; the target phase - correlation peak refers to the phase - correlation peak used to determine the rotation angle and the scaling factor of the carrier object;

[0316] Based on the target phase correlation peak, obtain the rotation angle and scaling factor of the carrier object.

[0317] Optionally, obtaining the target phase correlation peak according to the correlation between the self-synchronized autocorrelation image and the correlation peak template includes:

[0318] Determine whether the correlation between the self-synchronized autocorrelation image and the preset correlation peak template satisfies a preset correlation relationship. If it is satisfied, use the phase correlation peak corresponding to the self-synchronized autocorrelation image as the target phase correlation peak.

[0319] Optionally, the correlation between the self-synchronized autocorrelation image and the preset correlation peak template is: the weighted cross-correlation between the phase cross-correlation coefficient between the self-synchronized autocorrelation image and the preset correlation peak template and the spatial cross-correlation coefficient between the self-synchronized autocorrelation image and the correlation peak template.

[0320] Optionally, the preset correlation relationship is:

[0321] The difference between the weighted cross-correlation and a preset constant is less than a preset difference threshold.

[0322] Optionally, the electronic device further performs the following steps:

[0323] Sharpen the self-synchronized autocorrelation image to obtain the sharpened self-synchronized autocorrelation image;

[0324] Obtaining the target phase correlation peak according to the correlation between the self-synchronized autocorrelation image and the preset correlation peak template includes:

[0325] Obtain the target phase correlation peak according to the correlation between the sharpened self-synchronized autocorrelation image and the preset correlation peak template.

[0326] Optionally, the electronic device further performs the following steps: Obtain the autocorrelation image of the carrier object according to the carrier object.

[0327] Optionally, obtaining the autocorrelation peak image of the carrier object according to the carrier object includes:

[0328] Perform a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain signal of the carrier object;

[0329] Perform filtering processing on the frequency domain signal of the carrier object to obtain the autocorrelation image of the carrier object.

[0330] Optionally, the electronic device further performs the following steps: processing the autocorrelation image of the carrier object to remove noise, obtaining the autocorrelation image after noise removal;

[0331] The obtaining of the phase correlation peak according to the autocorrelation image of the carrier object and the preset correlation peak template includes:

[0332] Obtaining the phase correlation peak according to the autocorrelation image after noise removal and the correlation peak template;

[0333] The obtaining of the autocorrelation image after self-synchronization according to the autocorrelation image of the carrier object and the phase correlation peak includes:

[0334] Obtaining the autocorrelation image after self-synchronization according to the autocorrelation image after noise removal and the phase correlation peak.

[0335] Optionally, the obtaining of the autocorrelation image after self-synchronization according to the autocorrelation image after noise removal and the phase correlation peak includes:

[0336] Calculating the rotation angle and scaling factor of the autocorrelation image of the carrier object according to the phase correlation peak;

[0337] Performing rotation and scaling processing on the autocorrelation image of the carrier object according to the rotation angle and scaling factor to obtain the autocorrelation image after self-synchronization.

[0338] It should be noted that for the detailed description of the electronic device provided in the seventh embodiment of the present application, reference can be made to the relevant description of the second embodiment of the present application, which will not be elaborated here.

[0339] Corresponding to the data processing method provided in the second embodiment of the present application, the seventh embodiment of the present application provides a storage device storing a program of the data processing method, and the program is run by a processor to perform the following steps:

[0340] Obtaining a carrier object containing watermark information;

[0341] Obtaining the autocorrelation image after self-synchronization according to the autocorrelation image of the carrier object and the phase correlation peak; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template;

[0342] Obtaining the rotation angle and scaling factor of the carrier object according to the correlation between the autocorrelation image after self-synchronization and the correlation peak template;

[0343] Extracting the watermark information from the carrier object according to the rotation angle and scaling factor.

[0344] It should be noted that for a detailed description of the electronic device provided in the eighth embodiment of the present application, reference can be made to the relevant description of the second embodiment of the present application, which will not be elaborated here.

[0345] Although the present application is disclosed above in preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application should be determined by the scope defined by the claims of the present application.

[0346] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0347] Memory may include non-permanent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0348] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0349] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

Claims

1. A data processing method, characterized in that, Including: Obtaining a carrier object and target watermark information; Embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information; Obtaining the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect based on the carrier object containing the target watermark information; When the relationship does not meet the preset relationship, updating the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship, where the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information; The preset relationship is that the value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect; The parameter for characterizing the extracted watermark effect is obtained through the following method: Calculating the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image; Obtaining the parameter for characterizing the extracted watermark effect based on the detectable autocorrelation peak and the correlation peak template of the autocorrelation image, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak.

2. The method according to claim 1, wherein The step of embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information includes: Performing a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain coefficients of the carrier object; Embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength; Performing an inverse transformation process on the frequency domain coefficients embedded with the target watermark information to obtain a carrier object containing the target watermark information.

3. The method according to claim 2, characterized in that, The step of performing a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain coefficients of the carrier object includes: Performing a Fourier transform on the carrier object to obtain the Fourier coefficients of the carrier object; The step of embedding the target watermark information into the frequency domain coefficients of the carrier object according to the current watermark embedding strength includes: Embedding the target watermark information into the amplitude value of the Fourier coefficients of the carrier object according to the current embedding strength.

4. The method according to claim 1, characterized in that The step of updating the current watermark embedding strength includes: Adding a preset step size to the current watermark embedding strength as the updated current watermark embedding strength.

5. A data processing method, characterized in that, Including: Obtaining a carrier object containing watermark information; Obtaining a self-synchronized autocorrelation image based on the autocorrelation image and the phase correlation peak of the carrier object; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak; Obtaining the rotation angle and scaling coefficient of the carrier object according to the correlation between the self-synchronized autocorrelation image and the correlation peak template; Extracting the watermark information from the carrier object according to the rotation angle and scaling coefficient.

6. The method according to claim 5, wherein Obtaining the rotation angle and scaling coefficient of the carrier object according to the correlation between the self-synchronized self-correlation image and the correlation peak template includes: Obtaining a target phase correlation peak according to the correlation between the self-synchronized self-correlation image and the correlation peak template; the target phase correlation peak refers to the phase correlation peak used to determine the rotation angle and scaling coefficient of the carrier object; Obtaining the rotation angle and scaling coefficient of the carrier object according to the target phase correlation peak.

7. The method according to claim 6, characterized in that, Obtaining a target phase correlation peak according to the correlation between the self-synchronized self-correlation image and the correlation peak template includes: Judging whether the correlation between the self-synchronized self-correlation image and the correlation peak template meets a preset correlation relationship. If it meets, taking the phase correlation peak corresponding to the self-synchronized self-correlation image as the target phase correlation peak.

8. The method according to claim 7, wherein The correlation between the self-synchronized self-correlation image and the correlation peak template is: the weighted cross-correlation between the phase cross-correlation coefficient between the self-synchronized self-correlation image and the correlation peak template and the spatial cross-correlation coefficient between the self-synchronized self-correlation image and the correlation peak template.

9. The method according to claim 8, characterized in that The preset correlation relationship is: The difference between the weighted cross-correlation and a preset constant is less than a preset difference threshold.

10. The method according to claim 6, characterized in that, It further includes: Performing sharpening processing on the self-synchronized self-correlation image to obtain a sharpened self-synchronized self-correlation image; Obtaining a target phase correlation peak according to the correlation between the self-synchronized self-correlation image and the correlation peak template includes: Obtaining a target phase correlation peak according to the correlation between the sharpened self-synchronized self-correlation image and the correlation peak template.

11. The method according to claim 5, characterized in that, It further includes: Obtaining the self-correlation image of the carrier object according to the carrier object.

12. The method according to claim 11, wherein Obtaining the self-correlation peak image of the carrier object according to the carrier object includes: Performing a transformation from the spatial domain to the frequency domain on the carrier object to obtain the frequency domain signal of the carrier object; Performing filtering processing on the frequency domain signal of the carrier object to obtain the self-correlation image of the carrier object.

13. The method according to claim 12, wherein It further includes: Performing noise removal processing on the self-correlation image of the carrier object to obtain a noise-removed self-correlation image; Obtaining a phase correlation peak according to the self-correlation image of the carrier object and the correlation peak template includes: Obtaining a phase correlation peak according to the noise-removed self-correlation image and the correlation peak template; Obtaining the self-synchronized self-correlation image according to the self-correlation image of the carrier object and the phase correlation peak includes: Obtaining the self-synchronized self-correlation image according to the noise-removed self-correlation image and the phase correlation peak.

14. The method according to claim 13, wherein Obtaining the self-synchronized self-correlation image according to the noise-removed self-correlation image and the phase correlation peak includes: Calculating the rotation angle and scaling coefficient of the self-correlation image of the carrier object according to the phase correlation peak; Performing rotation and scaling processing on the self-correlation image of the carrier object according to the rotation angle and scaling coefficient to obtain the self-synchronized self-correlation image.

15. A data processing device, characterized in that, It includes: An information acquisition unit for acquiring a carrier object and target watermark information; A target watermark information embedding unit, configured to embed the target watermark information into the carrier object according to the current watermark embedding strength, so as to obtain a carrier object containing the target watermark information; A parameter relationship obtaining unit, configured to obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect according to the carrier object containing the target watermark information; A current watermark embedding strength updating unit, configured to update the current watermark embedding strength when the relationship does not meet the preset relationship until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship, where the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information; The preset relationship is that the value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect; The parameter for characterizing the extracted watermark effect is obtained by the following method: Calculating the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image; Obtaining the parameter for characterizing the extracted watermark effect according to the detectable autocorrelation peak and the correlation peak template of the autocorrelation image, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak.

16. An electronic device, characterized in that, Including: A processor; A memory, configured to store a program of a data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed: Obtaining a carrier object and target watermark information; Embedding the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information; Obtaining the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect according to the carrier object containing the target watermark information; When the relationship does not meet the preset relationship, updating the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect meets the preset relationship, where the parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information; The preset relationship is that the value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect; The parameter for characterizing the extracted watermark effect is obtained by the following method: Calculating the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image; Obtaining the parameter for characterizing the extracted watermark effect according to the detectable autocorrelation peak and the correlation peak template of the autocorrelation image, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak.

17. A storage device, storing a program of a data processing method. When the program is run by a processor, the following steps are executed: Obtaining a carrier object and target watermark information; Embed the target watermark information into the carrier object according to the current watermark embedding strength to obtain a carrier object containing the target watermark information; According to the carrier object containing the target watermark information, obtain the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the extracted watermark effect; When the relationship does not satisfy the preset relationship, update the current watermark embedding strength until the relationship between the parameter for characterizing the watermark hiding effect and the parameter for characterizing the watermark extraction effect satisfies the preset relationship, where The parameter for characterizing the watermark hiding effect is the structural similarity between the carrier object before watermark embedding and the carrier object containing the target watermark information; The preset relationship is that the value of the parameter for characterizing the extracted watermark effect is greater than the value of the parameter for characterizing the watermark hiding effect; The parameter for characterizing the extracted watermark effect is obtained by the following method: Calculate the autocorrelation of the carrier object containing the target watermark information at the current watermark embedding strength to obtain an autocorrelation image; According to the detectable autocorrelation peak and the correlation peak template of the autocorrelation image, obtain the parameter for characterizing the extracted watermark effect, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak.

18. A data processing device, characterized in that, Include: A carrier object obtaining unit, configured to obtain a carrier object containing watermark information; An autocorrelation image obtaining unit after self-synchronization, configured to obtain an autocorrelation image after self-synchronization according to the autocorrelation image and the phase correlation peak of the carrier object; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak; A rotation angle and scaling factor obtaining unit, configured to obtain the rotation angle and scaling factor of the carrier object according to the correlation between the autocorrelation image after self-synchronization and the correlation peak template; A watermark information extraction unit, configured to extract watermark information from the carrier object according to the rotation angle and scaling factor.

19. An electronic device, characterized in that, Include: A processor; A memory, configured to store a program of a data processing method. After the device is powered on and runs the program of the data processing method through the processor, the following steps are executed: Obtain a carrier object containing watermark information; According to the autocorrelation image and the phase correlation peak of the carrier object, obtain an autocorrelation image after self-synchronization; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template, where the correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak; According to the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template, obtain the rotation angle and scaling factor of the carrier object; According to the rotation angle and scaling factor, extract watermark information from the carrier object.

20. A storage device stores a program of a data processing method. When the program is run by a processor, the following steps are executed: Obtain a carrier object containing watermark information; An autocorrelation image after self-synchronization is obtained based on the autocorrelation image and the phase correlation peak of the carrier object; the phase correlation peak is obtained according to the autocorrelation image of the carrier object and a preset correlation peak template, where, The correlation peak template is an autocorrelation peak template automatically generated according to the position of the template autocorrelation peak; According to the correlation between the autocorrelation image after self-synchronization and the preset correlation peak template, obtain the rotation angle and scaling factor of the carrier object; Extract the watermark information from the carrier object according to the rotation angle and the scaling factor.

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