CN113887527A - Face image processing method and device, computer equipment and storage medium - Google Patents
Face image processing method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a face image processing method, a face image processing device, computer equipment and a storage medium, which are applied to the technical field of image processing and used for improving the accuracy of identifying a tampered face image. The method provided by the invention comprises the following steps: acquiring an original face image from a database, and adding a verification identifier to the original face image to obtain a verification face image; carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information; inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information; and obtaining a tampering judgment result based on the facial image to be verified and the verification probability information, and judging the tampering result of the original facial image based on the tampering judgment result.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method and an apparatus for processing a face image, a computer device, and a storage medium.
Background
With the development of science and technology, the artificial intelligence technology can replace the face in the image or the video with the face of other people, and the purpose of entertainment is achieved through face changing. The face counterfeiting is realized through an artificial intelligence technology, and when the face counterfeiting is applied to false news or fraud behaviors, the reputation of a citizen is damaged, and even economic loss is caused seriously.
In order to alleviate the risk of maliciously tampering the face image, the prior art detects the face image through deep learning or a neural network to judge whether the face image is tampered, the detection idea is too passive, the tampered image is basically identified according to a classifier, and if the image quality is poor, the identification effect is not high, and the identification rate is low.
Disclosure of Invention
The invention provides a face image processing method, a face image processing device, computer equipment and a storage medium, which are used for improving the accuracy of identifying a tampered face image.
A face image processing method comprises the following steps:
acquiring an original face image from a database, and adding a verification identifier to the original face image to obtain a verification face image;
carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information;
inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information;
and obtaining a tampering judgment result based on the facial image to be verified and the verification probability information, and judging the tampering result of the original facial image based on the tampering judgment result.
A face image processing apparatus comprising:
the identification module is used for acquiring an original face image from a database, and adding a verification identification to the original face image to obtain a verification face image;
the coding module is used for carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information;
the verification module is used for inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information;
and the judging module is used for obtaining a tampering judgment result based on the facial image to be verified and the verification probability information and judging the tampering result of the original facial image based on the tampering judgment result.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the above-mentioned face image processing method when executing said computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned face image processing method.
According to the face image processing method, the face image processing device, the computer equipment and the storage medium, the original face image is obtained from the database, and the verification identification is added to the original face image to obtain the verification face image; carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information; inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information; and obtaining a tampering judgment result based on the face image to be verified and the verification probability information, judging the tampering result of the original face image based on the tampering judgment result, constructing a face verification model through an attention mechanism, and judging whether the original face image is tampered or not through the face image to be verified and the verification probability information of the input original face image, so that the possibility of judging that the original face image is tampered is improved, and the accuracy of identifying the tampered face image is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a face image processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a face image processing method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a face image processing apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The face image processing method provided by the application can be applied to the application environment shown in fig. 1, wherein the terminal device communicates with the server through a network. The terminal device may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
The system framework 100 may include terminal devices, networks, and servers. The network serves as a medium for providing a communication link between the terminal device and the server. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use a terminal device to interact with a server over a network to receive or send messages or the like.
The terminal devices 101, 102, 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, E-book readers, MP3 players (Moving Picture E interface shows a properties Group Audio Layer III, motion Picture experts compress standard Audio Layer 3), MP4 players (Moving Picture E interface shows a properties Group Audio Layer IV, motion Picture experts compress standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the facial image processing method provided by the embodiment of the present invention is executed by a server, and accordingly, the facial image processing apparatus is disposed in the server.
It should be understood that the number of the terminal devices, the networks, and the servers in fig. 1 is only illustrative, and any number of the terminal devices, the networks, and the servers may be provided according to implementation requirements, and the terminal devices in the embodiment of the present invention may specifically correspond to an application system in actual production.
In an embodiment, as shown in fig. 2, a method for processing a face image is provided, which is described by taking the method applied to the server in fig. 1 as an example, and includes the following steps:
and S10, acquiring the original face image from the database, and adding a verification identifier to the original face image to obtain a verification face image.
Specifically, the database refers to a database, a server, and the like storing face images. And acquiring an original face image from the database, and adding a verification identifier on the original face image by adopting a preset method to obtain the verification face image added with the verification identifier. The original face image refers to a face image without added verification identification. The verification identifier may be a preset identifier, and specifically may be a watermark symbol or the like.
S20, the verification face image is subjected to image segmentation to obtain at least one segmented image, and each segmented image is encoded to obtain input encoding information.
Specifically, before the face image to be verified is input into the face verification model, the ruler needs to be adapted to the input structure of the face verification modelThe size (HxWxC high x wide x channel) verification face image is cut into a pxp segmentation image, and specifically, at least H x W/p is obtained2The image is block-divided to form an image sequence.
The image sequence formed by the divided images is transformed through a linear transformation layer, the transformed result is called patchenbelling, each divided image generates a position embedding according to the position in the image sequence, and the patchenbelldig and the position embedding are added to obtain carelessness coding information.
And S30, inputting the input coding information into a preset human face verification model to obtain a human face image to be verified and verification probability information.
Specifically, the input coding information is input into a preset face verification model, and a face image to be verified and verification probability information are obtained.
The preset human face verification model is constructed based on a transform model (a model based on a multi-head attention mechanism), wherein the transform model comprises a self-attention mechanism, and shows strong competitiveness in image classification, image detection, video processing, unsupervised target discovery and unified text vision tasks.
The Transformer model is basically composed of an Encoder-Decoder, and is composed of a plurality of Transformer layers. The Transformer layer first normalizes the input using the layer norm and then calculates V, K, Q as the input to the multi-attribute head using the input and the parameters PV, PK, PQ, respectively. The Q, K and V parameters are calculated according to the following formula:
specifically, the face image to be verified is a face image which is output by a face verification model after the face image is verified, a verification identifier in the face image to be verified is removed, and verification probability information is output through a classification layer, the verification probability information refers to the possibility that the original face image is tampered, the probability value of 0-1 indicates that the closer to 0, the lower the possibility that the original face image is tampered, and the closer to 1, the higher the possibility that the original face image is tampered.
And S40, obtaining a tampering judgment result based on the face image to be verified and the verification probability information, and judging the tampering result of the original face image based on the tampering judgment result.
Specifically, after the face image to be verified is processed by the face verification model, the face image to be verified is obtained, the image quality of the face image to be verified is affected by the original input image, and if the face in the original face image is tampered, the image quality of the obtained face image with verification is reduced after the face image is added with the verification identifier and processed by the face verification model.
The face image to be verified and the verification probability information are subjected to double judgment to obtain a tampering judgment result, whether the original face image is tampered or not is further accurately identified, and the accuracy of image tampering is guaranteed.
According to the face image processing method provided by the embodiment of the invention, an original face image is obtained from a database, and a verification identifier is added to the original face image to obtain a verification face image; carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information; inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information; and obtaining a tampering judgment result based on the face image to be verified and the verification probability information, judging the tampering result of the original face image based on the tampering judgment result, constructing a face verification model through an attention mechanism, and judging whether the original face image is tampered or not through the face image to be verified and the verification probability information of the input original face image, so that the possibility of judging that the original face image is tampered is improved, and the accuracy of identifying the tampered face image is improved.
Further, in this embodiment, as an optional implementation manner, in step S10, the verifying identifier includes a checkered template image, the obtaining the original face image from the database, and adding the verifying identifier to the original face image to obtain the verified face image includes:
s101, acquiring a preset reticulate pattern template image, and fusing the reticulate pattern template image and an original face image through an image synthesis technology to obtain a verified face image.
Specifically, one or more kinds of mesh pattern patterns may be preset in the mesh template image, and when the verification identifier is added, one kind of mesh pattern template is randomly selected to perform image synthesis.
Furthermore, aligning the reticulate pattern template image with the center of the original face image, and fusing the reticulate pattern template image with the original face image to obtain the verified face image.
The process of generating the verification face image can be expressed by the following formula:
the face _ img represents a face image, the mask _ img represents a mesh template image, wherein a mesh position pixel value is 255, a non-mesh position pixel value is 0, alpha is 0.1, and beta is 0.2. i and j represent the coordinates of each pixel in the image.
In this embodiment, the reticulate pattern template image is randomly generated to improve the randomness, and then the reticulate pattern template image is fused with the original face image to generate the verification face image.
Further, in this embodiment, as an optional implementation manner, in step S20, performing image segmentation on the verification face image to obtain at least one segmented image, and encoding each segmented image to obtain the input encoding information includes:
s201, carrying out image segmentation on the verified face image to obtain a plurality of segmented images.
And S202, carrying out position coding on the segmentation image based on the position of the segmentation image in the verification face image.
And S203, extracting the characteristics of the segmented images to obtain the characteristic information of each segmented image.
And S204, combining the characteristic information and the position codes for each segmented image to obtain input code information.
Specifically, a verification face image is segmented to adapt to an input structure of a face verification model, a plurality of segmented images are obtained and form an image sequence, position encoding parameters, namely position embedding, are generated for each segmented image according to the position of each segmented image in the image sequence, 256-dimensional features are obtained through mapping processing of the segmented images, and feature information, namely the feature embedding, is formed.
And adding the position embedding and the characteristic embedding to obtain input data of the face verification model, namely input coding information.
In this embodiment, the verified face image is segmented to be used as an input image of the face verification model, and a target output is obtained, so that a result of identifying whether the input face image is tampered is obtained quickly.
Further, in this embodiment, as an optional implementation manner, before step S30, the method includes:
and S1, acquiring training face images from a preset database, wherein the training face images comprise a group of original images and corresponding verification images.
And S2, constructing a face verification model based on the attention mechanism, inputting the training face image into the face verification model for training, and obtaining a preset face verification model.
Specifically, the training face image includes a group of face images and verification images of the face images after verification identifiers are added to the face images, and in this embodiment, the verification identifiers are random checkered template images.
And constructing a face verification model according to an attention mechanism, and training the face verification model through the training face image to obtain a preset face verification model.
In the embodiment, the face verification model is constructed through the attention mechanism, and whether the face image is tampered or not is recognized through the face verification model, so that the possibility and the accuracy of recognizing tampering operation are improved.
Further, in this embodiment, as an optional implementation manner, in step S30, the inputting encoding information is input into a preset face verification model, and the obtaining of the face image to be verified and the verification probability information includes:
s301, inputting the input coding information into a coding layer of a preset human face verification model, outputting a human face image to be verified, and outputting verification probability information through a classification layer.
Specifically, the face image to be verified is output through the coding layer, the verification probability information is output through the classification layer, two evidences for recognizing the tampering trace are output through the coding layer and the classification layer respectively, and the accuracy of the recognition result is guaranteed through double verification.
Further, in this embodiment, as an optional implementation manner, in step S40, the obtaining a tampering determination result based on the facial image to be verified and the verification probability information, and determining the tampering result of the original facial image based on the tampering determination result includes:
s401, based on the corresponding relation between the verification probability information and the tampering result, obtaining a first judgment result according to the verification probability information.
S402, judging the image quality of the face image to be verified to obtain a quality score, and obtaining a second judgment result corresponding to the quality score according to a preset corresponding relation.
And S403, obtaining a tampering judgment result based on the first judgment result and the second judgment result.
Specifically, the verification probability information represents the possibility of tampering of the input image in a numerical value mode, and a value of 0-1 is obtained as the verification probability information according to the sigmod function constraint.
The face images without added verification identification are processed by a preset face verification model to obtain low-quality face images to be verified, and the image quality of the face images to be verified is reduced.
The method comprises the steps of performing Quality scoring on a face Image to be verified, wherein the Quality scoring refers to Image Quality Assessment (IQA) on the face Image, the IQA is one of basic technologies in Image processing, and the Quality (Image distortion degree) of the Image is evaluated mainly by performing characteristic analysis research on the Image.
The image grading rules can be set according to actual requirements, and image quality grading is carried out on the face images according to the image grading rules to obtain image definition grading. The image scoring rules may include a plurality of scoring dimensions, including but not limited to: and the image definition dimension, the resolution dimension and the like are used for scoring the face image from all scoring dimensions to obtain the image definition score of the face image.
And generating the tampering possibility corresponding to each score according to the quality scores to obtain a second judgment result.
And adding the first judgment result and the second judgment result to obtain a final tampering judgment result.
In the embodiment, the final tampering result is obtained through the two judgment bases, so that the reliability of the final tampering result is ensured to a certain extent, and the accuracy of the identified tampering result is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a face image processing apparatus is provided, and the face image processing apparatus corresponds to the face image processing method in the above embodiment one to one. As shown in fig. 3, the facial image processing apparatus includes an identification module 31, an encoding module 32, a verification module 33 and a judgment module 34. The functional modules are explained in detail as follows:
and the identification module 31 is configured to acquire an original face image from the database, and add a verification identification to the original face image to obtain a verified face image.
And the coding module 32 is configured to perform image segmentation on the verified face image to obtain at least one segmented image, and code each segmented image to obtain input coding information.
And the verification module 33 is configured to input the input coding information into a preset face verification model, so as to obtain a face image to be verified and verification probability information.
And the judging module 34 is configured to obtain a tampering judgment result based on the facial image to be verified and the verification probability information, and judge a tampering result of the original facial image based on the tampering judgment result.
Further, the identification module 31 includes the following units:
and the identification synthesis unit is used for acquiring a preset reticulate pattern template image, and fusing the reticulate pattern template image and the original face image through an image synthesis technology to obtain a verified face image.
Further, the encoding module 32 includes the following units:
and the segmentation unit is used for carrying out image segmentation on the verified face image to obtain a plurality of segmented images.
And the coding unit is used for carrying out position coding on the segmentation images based on the positions of the segmentation images in the verification face image.
And the characteristic extraction unit is used for extracting the characteristics of the segmented images to obtain the characteristic information of each segmented image.
And the input coding unit is used for combining the characteristic information and the position codes for each divided image to obtain input coding information.
Further, the verification module 33 includes the following units:
and the verification information output unit is used for inputting the input coding information into a coding layer of a preset human face verification model, outputting the human face image to be verified and outputting verification probability information through a classification layer.
Further, the determining module 34 includes the following units:
and the first judgment result unit is used for obtaining a first judgment result according to the verification probability information based on the corresponding relation between the verification probability information and the tampering result.
And the second judgment result unit is used for judging the image quality of the face image to be verified to obtain a quality score and obtaining a second judgment result corresponding to the quality score according to the preset corresponding relation.
And a judgment result unit for obtaining a tampering judgment result based on the first judgment result and the second judgment result.
Further, the face image processing device further comprises the following modules:
and the verification data acquisition module is used for acquiring a training face image from a preset database, wherein the training face image comprises a group of original images and corresponding verification images.
And the model training module is used for constructing a face verification model based on an attention mechanism, inputting a training face image into the face verification model for training, and obtaining a preset face verification model.
Wherein the meaning of "first" and "second" in the above modules/units is only to distinguish different modules/units, and is not used to define which module/unit has higher priority or other defining meaning. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such process, method, article, or apparatus, and such that a division of modules presented in this application is merely a logical division and may be implemented in a practical application in a further manner.
For specific limitations of the facial image processing apparatus, reference may be made to the above limitations of the facial image processing method, which are not described herein again. All or part of the modules in the human face image processing device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data involved in the face image processing method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a face image processing method.
In one embodiment, there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the steps of the face image processing method in the above embodiments, such as the steps S10 to S40 shown in fig. 2 and other extensions of the method and related steps. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units of the face image processing apparatus in the above-described embodiments, such as the functions of the modules 31 to 34 shown in fig. 3. To avoid repetition, further description is omitted here.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc.
The memory may be integrated in the processor or may be provided separately from the processor.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the face image processing method in the above-described embodiments, such as the steps S10 through S40 shown in fig. 2 and extensions of other extensions and related steps of the method. Alternatively, the computer program, when executed by the processor, implements the functions of the modules/units of the face image processing apparatus in the above-described embodiments, such as the functions of the modules 31 to 34 shown in fig. 3. To avoid repetition, further description is omitted here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A face image processing method is characterized by comprising the following steps:
acquiring an original face image from a database, and adding a verification identifier to the original face image to obtain a verification face image;
carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information;
inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information;
and obtaining a tampering judgment result based on the facial image to be verified and the verification probability information, and judging the tampering result of the original facial image based on the tampering judgment result.
2. The method according to claim 1, wherein the verification identifier comprises a checkered template image, and the step of adding the verification identifier to the original face image to obtain the verification face image comprises:
acquiring a preset reticulate pattern template image, and fusing the reticulate pattern template image and the original face image through an image synthesis technology to obtain a verified face image.
3. The method of claim 1, wherein the step of performing image segmentation on the verification face image to obtain at least one segmented image, and encoding each segmented image to obtain the input encoding information comprises:
carrying out image segmentation on the verification face image to obtain a plurality of segmentation images;
based on the position of the segmentation image in the verification face image, carrying out position coding on the segmentation image;
performing feature extraction on the segmented images to obtain feature information of each segmented image;
and combining the characteristic information and the position code aiming at each segmented image to obtain input code information.
4. The method for processing the human face image according to claim 1, wherein before the step of inputting the input coding information into a preset human face verification model to obtain the human face image to be verified and verification probability information, the method comprises:
acquiring a training face image from a preset database, wherein the training face image comprises a group of original images and corresponding verification images;
and constructing a face verification model based on an attention mechanism, inputting the training face image into the face verification model for training, and obtaining a preset face verification model.
5. The method for processing the human face image according to claim 1, wherein the step of inputting the input coding information into a preset human face verification model to obtain the human face image to be verified and verification probability information comprises:
and inputting the input coding information into a coding layer of the preset human face verification model, outputting a human face image to be verified, and outputting verification probability information through a classification layer.
6. The method according to claim 1, wherein the step of obtaining a tampering determination result based on the face image to be verified and the verification probability information, and determining the tampering result of the original face image based on the tampering determination result comprises:
obtaining a first judgment result according to the verification probability information based on the corresponding relation between the verification probability information and the tampering result;
performing image quality judgment on the facial image to be verified to obtain a quality score, and obtaining a second judgment result corresponding to the quality score according to a preset corresponding relation;
and obtaining a tampering judgment result based on the first judgment result and the second judgment result.
7. A face image processing apparatus, comprising:
the identification module is used for acquiring an original face image from a database, and adding a verification identification to the original face image to obtain a verification face image;
the coding module is used for carrying out image segmentation on the verification face image to obtain at least one segmented image, and coding each segmented image to obtain input coding information;
the verification module is used for inputting the input coding information into a preset face verification model to obtain a face image to be verified and verification probability information;
and the judging module is used for obtaining a tampering judgment result based on the facial image to be verified and the verification probability information and judging the tampering result of the original facial image based on the tampering judgment result.
8. The facial image processing apparatus according to claim 7, wherein said encoding module comprises the following units:
the segmentation unit is used for carrying out image segmentation on the verification face image to obtain a plurality of segmentation images;
the coding unit is used for carrying out position coding on the segmentation image based on the position of the segmentation image in the verification face image;
the characteristic extraction unit is used for extracting the characteristics of the segmentation images to obtain the characteristic information of each segmentation image;
and an input encoding unit configured to combine the feature information and the position code for each of the divided images to obtain input encoding information.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the face image processing method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the face image processing method according to any one of claims 1 to 6.
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