WO2020147445A1 - 翻拍图像识别方法、装置、计算机设备和计算机可读存储介质 - Google Patents

翻拍图像识别方法、装置、计算机设备和计算机可读存储介质 Download PDF

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WO2020147445A1
WO2020147445A1 PCT/CN2019/122696 CN2019122696W WO2020147445A1 WO 2020147445 A1 WO2020147445 A1 WO 2020147445A1 CN 2019122696 W CN2019122696 W CN 2019122696W WO 2020147445 A1 WO2020147445 A1 WO 2020147445A1
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image
recognized
probability
lbp
remake
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French (fr)
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徐国诚
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OneConnect Smart Technology Co Ltd
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OneConnect Smart Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features

Definitions

  • This application relates to a method, device, computer equipment, and computer-readable storage medium for re-photographing image recognition.
  • opening an account online or handling important business online may require users to shoot and upload corresponding images through mobile terminals or web cameras, such as the user’s ID image or the user’s real image .
  • the uploaded image may not be a real image obtained by the user by shooting himself, or a credential image obtained by shooting a real ID, but a picture displayed on the display of a computer or mobile phone.
  • the uploaded image may not be a real image obtained by the user by shooting himself, or a credential image obtained by shooting a real ID, but a picture displayed on the display of a computer or mobile phone.
  • a method, apparatus, computer equipment, and computer-readable storage medium for recognizing a remake image are provided.
  • a method for recognizing a remake image which is executed by a server, and the method includes:
  • the probability threshold it is determined that the image to be recognized is a copying image.
  • a re-photographed image recognition device includes:
  • Image acquisition module for acquiring the image to be recognized
  • a transform module which is used to perform Fourier transform on the acquired image to be recognized to obtain a Fourier spectrum characteristic map
  • the feature value extraction module is configured to extract the feature value of the local binary mode LBP from the Fourier spectrum feature map
  • a generating module for generating LBP histogram statistical data representing the statistical probability of the LBP feature value according to the extracted LBP feature value
  • a processing module configured to input the LBP histogram statistical data into a neural network model, and perform identification processing on the LBP histogram statistical data through the neural network model to obtain the first remake probability;
  • the remake image determination module is configured to determine that the to-be-identified image is a remake image when the first remake probability reaches a probability threshold.
  • a computer device includes a memory and a processor, the memory stores computer-readable instructions, and the processor implements the following steps when executing the computer-readable instructions:
  • the probability threshold it is determined that the image to be recognized is a copying image.
  • a non-volatile computer-readable storage medium having computer-readable instructions stored thereon, and when the computer-readable instructions are executed by a processor, the following steps are implemented:
  • the probability threshold it is determined that the image to be recognized is a copying image.
  • Fig. 1 is an application scene diagram of the method for recognizing a re-photographed image according to one or more embodiments.
  • Fig. 2 is a schematic flowchart of a method for recognizing a re-photographed image according to one or more embodiments.
  • Fig. 3 is a schematic diagram of extracting LBP features according to one or more embodiments.
  • Fig. 4 is a schematic flowchart of the steps of training a neural network model according to another or more embodiments.
  • Fig. 5 is a structural block diagram of an image recognizing device according to one or more embodiments.
  • Fig. 6 is a structural block diagram of an image recognizing device according to another or more embodiments.
  • Fig. 7 is an internal structure diagram of a computer device according to one or more embodiments.
  • the image recognition method provided by this application can be applied in the application environment as shown in FIG. 1.
  • the terminal 102 communicates with the server 104 through the network through the network.
  • the server 104 obtains the image to be recognized through the terminal 102, and performs Fourier transform on the obtained image to be recognized to obtain a Fourier spectrum feature map;
  • the server 104 extracts LBP (Local Binary Pattern, local binary pattern) features from the Fourier spectrum feature map Calculate the statistical probability of the LBP feature value within the range of each feature value, and input the statistical probability into the neural network model.
  • the neural network model can determine whether the image to be recognized is a remake image.
  • the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers.
  • a method for recognizing a remake image is provided. Taking the method applied to the server in FIG. 1 as an example for description, the method includes the following steps:
  • the image to be recognized may be a 32-bit color image including RGB channels, in addition, it may also be a 64-bit or 128-bit color image including RGB channels.
  • the server captures the image to be recognized through a camera installed on the terminal; or, the server obtains the image to be recognized from a local album stored in the terminal through the terminal.
  • the method for recognizing the re-photographed image is applied to a user registered account (such as a registered financial account)
  • a user registered account such as a registered financial account
  • the function options of the image and the other is the function option that supports the user to select the image from the album.
  • the terminal calls the camera to capture the image of the user or user ID, and uses the captured image as the image to be recognized.
  • the terminal obtains the image of the user or the user certificate from the local album, and uses the obtained image as the image to be recognized.
  • the account opening system will display an operation page for uploading the user's credential image and the user's real image.
  • the camera of the terminal can be called for the user to take the image, or the corresponding image to be recognized can be selected from the album through the function options provided on the operation page, or the camera of the terminal can be called So that the user can take images.
  • S204 Perform Fourier transform on the acquired image to be identified to obtain a Fourier spectrum characteristic map.
  • the Fourier spectral characteristic map obtained after Fourier transform may be a single-channel 8-bit, 16-bit, or 32-bit grayscale image, and the spectral characteristic map reflects the aggregation of spectral energy points.
  • the Fourier transform may be a discrete Fourier transform.
  • S204 may specifically include: determining the size of the image to be recognized; when the size of the image to be recognized is larger than the first preset size or smaller than the second preset size, perform the image recognition according to the preset standard size.
  • Scaling Decompose the scaled image to be recognized into three images of RGB channels; perform Fourier transform on the three images respectively, and synthesize the transformed image; convert the synthetic image into a single channel Fourier spectrum characteristic map.
  • the terminal before performing Fourier transform on the acquired image, the terminal first determines the size of the image to be recognized, such as the image resolution or size. If the size of the image to be recognized is too large or too small, the image is enlarged or compressed to a preset standard size. The terminal decomposes the scaled image into three corresponding images according to the RGB three channels, performs Fourier transform on each image, and then synthesizes the changed images to obtain a combined feature map. The terminal performs gray scale conversion on this combined feature map, so that a single-channel Fourier spectrum feature map can be obtained. In the foregoing embodiment, compressing an image that is too large can reduce the amount of calculation in the recognition process.
  • the size of the image to be recognized such as the image resolution or size. If the size of the image to be recognized is too large or too small, the image is enlarged or compressed to a preset standard size.
  • the terminal decomposes the scaled image into three corresponding images according to the RGB three channels, performs Fourier transform on each image, and then
  • the server evenly divides the Fourier spectrum feature map into a plurality of sub Fourier spectrum feature blocks, and extracts the LBP feature value at the pixel point of each sub Fourier spectrum feature block.
  • the server divides the Fourier spectrum feature map into multiple sub-Fourier spectrum feature blocks, and divides each sub Fourier spectrum feature block into multiple pixel blocks.
  • the server judges whether the gray value of the non-central pixel is greater than the gray value of the central pixel; if it is, the gray value of the non-central pixel is set to the first value; if not, it is The gray value of the central pixel is set to the second value.
  • the server performs a weighted summation on the gray values of the non-central pixel points in each pixel block after the gray value is set, and uses the result of the weighted summation as the LBP feature value of each pixel block.
  • the server takes the pixel as the center pixel, and the gray value of the center pixel is compared with each field in the 3 ⁇ 3 window.
  • the gray values of the pixels are compared. If the gray value of the field pixel is greater than or equal to the gray value of the center pixel, the gray value of the field pixel is set to 1; if the gray value of the field pixel is less than For the gray value of the central pixel, the gray value of the domain pixel is set to 0. Therefore, the gray value of the central pixel is calculated as 01111100 in binary and 124 in decimal.
  • S208 Generate LBP histogram statistical data representing the statistical probability of the LBP feature value according to the extracted LBP feature value.
  • the server may sort the LBP feature values in each sub-Fourier spectral feature block in order of size; sort the LBP feature values in each sub-Fourier spectral feature block after sorting according to a preset step size Divide evenly into multiple characteristic value ranges; calculate the statistical probability of LBP characteristic values belonging to each characteristic value range.
  • an LBP histogram is established according to the statistical probability of the LBP feature value in each feature value range, and the probability corresponding to each LBP histogram is stored in a different sub-array.
  • the statistical probability of the LBP feature value of each sub-Fourier spectrum feature block within each preset feature value range is obtained, and the statistical probability may be LBP histogram statistical data.
  • the server saves the sub-arrays A1, A2...An in another array S.
  • S210 Input the statistical data of the LBP histogram into the neural network model, and perform identification processing on the statistical data of the LBP histogram through the neural network model to obtain the first remake probability.
  • the server inputs the LBP histogram statistical data into the trained neural network model in the form of an array.
  • the probability threshold is 90%
  • the image to be recognized is a remake image.
  • the image to be recognized is not a copy image.
  • the method further includes: the server generates an image review request carrying the image to be recognized; and sends the image review request to the reviewer account, so that the reviewer can The image to be recognized is reviewed and the review result is fed back; the review result for the image review request that is fed back by the reviewer’s account is received; the review result carries the second probability that the image to be recognized is a remake image; obtains the first image corresponding to the machine recognition A weight and a second weight corresponding to the recheck identification; according to the first weight and the second weight, the first and second remake probability are weighted and summed to obtain the weighted sum of the remake probability; when the weighted sum is greater than or equal to When the weighted sum is preset, it is finally determined that the image to be recognized is a remake image.
  • the first weight and the second weight are two different values, and the sum of the first weight and the second weight is 1.
  • the machine when the machine recognizes the image to be recognized as a remake, it will also send the image to be recognized to a professional, who will further review and recognize the image to be recognized, and then the feedback will contain the recognition result
  • the result of the review, the recognition result may be the probability that the image to be recognized is a remake image. If the probability that the image to be recognized is a re-photographed image is high (for example, greater than 90%), it is finally determined that the image to be recognized is a re-photographed image.
  • the weighted sum p is greater than or equal to the preset weighted sum, it is finally determined that the image to be recognized is a remake image.
  • the weighted sum p is less than the preset weighted sum, it is finally determined that the image to be recognized is not a remake image.
  • the server may determine whether the image to be recognized is a remake image based on the result of the review and recognition. When the first reproduction probability reaches the probability threshold, the server may also not need to perform the process of rechecking and identifying, and directly determine that the image to be recognized is a reproduced image.
  • this method can be applied to financial account opening.
  • the server rejects the user’s account opening request; when it is determined that the image to be recognized is a non-replicated image, the server further reviews other materials submitted by the user, and if they meet the account opening requirements, the user’s request is allowed. Account opening request.
  • (1) first divide the Fourier spectral feature map into multiple sub-Fourier spectral feature blocks, and then divide the sub-Fourier spectral feature block into multiple pixel blocks; (2) in each pixel block, The gray value of the central pixel is compared with the gray value of the adjacent 8 pixels. If the gray value of any one of the 8 pixels is greater than the gray value of the central pixel, the corresponding pixel The gray value of the point is set to 1, otherwise it is set to 0.
  • 8 pixels in the neighborhood of a 3 ⁇ 3 window can generate 8-bit binary numbers, that is, the LBP feature value of the center pixel of the window is obtained; (3) Then calculate the statistical histogram of each pixel block, the statistical histogram It is used to indicate the frequency of occurrence of the LBP feature value within each preset feature value range, that is, the statistical probability; wherein, the histogram can also be normalized. (4) Finally, according to the obtained statistical histogram of each pixel block, the LBP texture feature vector of the entire image to be recognized is obtained, and the LBP texture feature vector is input to the neural network model. Through the processing of the neural network, it can be obtained whether the image to be recognized is To remake an image.
  • a Fourier spectrum feature map is generated according to the acquired image to be recognized, and the LBP feature value is extracted from the Fourier spectrum feature map, so as to obtain the local texture feature of the image to be recognized.
  • the neural network model can determine whether the image to be recognized is a remake image, thereby avoiding the image to be recognized as a remake computer Or the pictures displayed on the display screens of devices such as mobile phones, so as to ensure the authenticity of the images and avoid the hidden dangers of user information caused by third-party use of re-photographed images.
  • the accuracy of re-photographed image recognition can be effectively improved, and the accuracy is increased from the original 80% to 93%.
  • the method further includes:
  • S402 Obtain a sample image to be identified, label the sample image to be identified, and obtain a sample image to be identified including a label; the label is used to indicate whether the sample image to be identified is a remake image.
  • the sample image to be identified may be a 32-bit color image including RGB channels, in addition, it may also be a 64-bit or 128-bit color image including RGB channels.
  • the size of the sample image to be identified is the preset standard size.
  • S404 Perform Fourier transform on the sample image to be identified containing the label to obtain a sample image of Fourier spectrum characteristics.
  • the method may further include: the server randomly rotates the sample image to be identified containing the label from 0 to 360 degrees, and processes such as random zooming, adjusting brightness, chroma, and clarity, etc. Then, according to the processed sample image to be identified, the corresponding Fourier spectrum feature sample image is generated to increase the generalization ability of the neural network model, thereby improving the accuracy of remake recognition.
  • S406 Extract LBP feature sample values from the Fourier spectrum feature sample map.
  • the Fourier spectrum feature sample map is evenly divided into a plurality of sub-Fourier spectrum feature sample blocks, and the LBP feature sample value is extracted from the pixels of each sub-Fourier spectrum feature sample block.
  • the Fourier spectrum feature sample image is equally divided into a plurality of sub-Fourier spectrum feature sample blocks; each sub-Fourier spectrum feature sample block is equally divided into a plurality of sample pixel blocks; in each sample pixel Block, judge whether the gray value of the non-central pixel is greater than the gray value of the central pixel; if so, set the gray value of the non-central pixel to the first value; if not, set the gray value of the non-central pixel The gray value is set to the second value; the gray value of the non-central pixel in each sample pixel block after the gray value is set is weighted and summed; the result of the weighted sum is used as the LBP feature sample value of each sample pixel block .
  • the gray value of the center pixel is respectively compared with that in the 3 ⁇ 3 window.
  • the gray values of pixels in each area are compared. If the gray value of the area pixel is greater than or equal to the gray value of the center pixel, the gray value of the area pixel is set to 1; if the gray value of the area pixel is If the value is less than the gray value of the central pixel, the gray value of the domain pixel is set to 0. Therefore, the gray value of the central pixel is calculated as 01111100 in binary and converted to decimal as 124, so each sub The LBP feature sample value of the Fourier spectrum feature sample block.
  • S408 Generate an LBP histogram statistical data sample for representing the statistical probability of the LBP feature value according to the extracted LBP feature value.
  • S208 may specifically include: the server may sort the LBP feature sample values in each sub-Fourier spectrum feature sample block in order of size; and sort the sorted sub-Fourier spectrum feature sample graphs
  • the LBP feature sample value in the block is evenly divided into multiple feature value ranges according to the preset step length; the statistical probability of the LBP feature sample value belonging to each feature value range is calculated.
  • the LBP histogram is established according to the statistical probability of the LBP feature value in each feature value range, and the probability corresponding to each LBP histogram is stored in a different In the sub-arrays A1, A2...An of, the statistical probability of the LBP feature sample value of each sub-Fourier spectrum feature sample block within each preset feature value range is obtained.
  • the server saves the sub-arrays A1, A2...An in a large array S.
  • S410 Input the LBP histogram statistical data sample into the neural network model, and perform identification processing on the LBP histogram statistical data sample through the neural network model to obtain the third remake probability.
  • the server inputs the LBP histogram statistical data samples into the trained neural network model in the form of an array.
  • S412 Compare the difference between the third remake probability and the label, and adjust the parameters of the neural network model.
  • S412 includes: determining the error between the third remake probability and the label; propagating the error back to the network layer of the neural network model to obtain the gradient of each network layer parameter; adjusting the neural network according to the obtained gradient The parameters of each network layer in the network model.
  • the server can determine the error between the third remake probability and the label through the loss function.
  • the loss function can be any of the following: Mean Squared Error (Mean Squared Error), cross-entropy loss function, L2Loss function, and Focal Loss function.
  • a remake image recognition device including: image acquisition module 502, transformation module 504, feature value extraction module 506, generation module 508, processing module 510, and remake image determination Module 512, of which:
  • the image acquisition module 502 is used to acquire the image to be recognized
  • the transform module 504 is configured to perform Fourier transform on the acquired image to be recognized to obtain a Fourier spectrum characteristic map
  • the feature value extraction module 506 is used to extract the feature value of the local binary mode LBP from the Fourier spectrum feature map
  • the generating module 508 is configured to generate LBP histogram statistical data representing the statistical probability of the LBP feature value according to the extracted LBP feature value;
  • the processing module 510 is configured to input LBP histogram statistical data into a neural network model, and perform recognition processing on the LBP histogram statistical data through the neural network model to obtain the first remake probability;
  • the remake image determination module 512 is configured to determine that the image to be recognized is a remake image when the first remake probability reaches the probability threshold.
  • the transformation module 504 is further configured to: determine the size of the image to be recognized; when the size of the image to be recognized is larger than the first preset size, or smaller than the second preset size, it is to be recognized according to the preset standard size
  • the image is scaled; the scaled image to be recognized is decomposed into three images of RGB channel; the three images are respectively Fourier transformed, and the transformed image is synthesized; the synthesized image is converted into a single Fourier spectrum characteristic map of the channel.
  • the feature value extraction module 506 is further configured to: divide the Fourier spectrum feature map into a plurality of sub-Fourier spectrum feature blocks; respectively divide each sub-Fourier spectrum feature block into a plurality of pixel blocks; In each pixel block, determine whether the gray value of the non-central pixel is greater than the gray value of the central pixel; if so, set the gray value of the non-central pixel to the first value; if not, set the non-central pixel The gray value of the dot is set to the second value; the gray value of the non-central pixel point in each pixel block after the gray value is set is weighted and summed; the result of the weighted sum is used as the LBP feature value of each pixel block.
  • the device further includes: a request generating module 514, a sending module 516, a receiving module 518, a weight obtaining module 520, and a weighting module 522, where:
  • the request generation module 514 is configured to generate an image review request carrying the image to be recognized
  • the sending module 516 is used to send an image review request
  • the receiving module 518 is configured to receive a review result in response to an image review request; the review result carries a second remake probability that the image to be identified is a remake image;
  • the weight obtaining module 520 is configured to obtain the first weight corresponding to the machine recognition and the second weight corresponding to the review recognition;
  • the weighting module 522 performs a weighted summation of the first remake probability and the second remake probability according to the first weight and the second weight respectively to obtain the weighted sum of the remake probability;
  • the re-photographed image determining module 512 is further configured to finally determine that the image to be recognized is a re-photographed image when the weighted sum is greater than or equal to the preset weighted sum.
  • the device further includes: an adjustment module 524, wherein:
  • the image acquisition module 502 is also used for the image acquisition module to acquire a sample image to be identified, annotate the sample image to be identified, and obtain a sample image to be identified that includes a label; the label is used to indicate whether the sample image to be identified is a remake image;
  • the transform module 504 is further configured to perform Fourier transform on the sample image to be identified containing the label to obtain a sample image of Fourier spectrum characteristics;
  • the feature value extraction module 504 block is also used to extract LBP feature sample values from the Fourier spectrum feature sample map
  • the generating module 508 is further configured to generate an LBP histogram statistical data sample for representing the statistical probability of the LBP feature value according to the extracted LBP feature value;
  • the processing module 510 is further configured to input LBP histogram statistical data samples into the neural network model, and perform identification processing on the LBP histogram statistical data samples through the neural network model to obtain the third remake probability;
  • the adjustment module 524 is used to compare the difference between the third remake probability and the label, and adjust the parameters of the neural network model.
  • the adjustment module 524 is further used to: determine the error between the third remake probability and the label; backpropagate the error to the network layer of the neural network model to obtain the gradient of each network layer parameter; The gradient of adjusts the parameters of each network layer in the neural network model.
  • a Fourier spectrum feature map is generated according to the acquired image to be recognized, and the LBP feature value is extracted from the Fourier spectrum feature map, so as to obtain the local texture feature of the image to be recognized.
  • the neural network model can determine whether the image to be recognized is a remake image, thereby avoiding the image to be recognized as a remake computer Or the pictures displayed on the display screens of devices such as mobile phones, so as to ensure the authenticity of the images and avoid the hidden dangers of user information caused by third-party use of re-photographed images.
  • the accuracy of re-photographed image recognition can be effectively improved, and the accuracy is increased from the original 80% to 93%.
  • Each module in the above-mentioned re-photographed image recognition device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 7.
  • the computer device includes a processor, memory, network interface, and database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, computer-readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium.
  • the database of the computer equipment is used to store data such as images to be identified and sample images to be identified.
  • the network interface of the computer device is used to communicate with external terminals through a network connection.
  • the computer-readable instruction is executed by the processor to realize a re-photographed image recognition method.
  • FIG. 7 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or less parts than shown in the figure, or combining some parts, or having a different part arrangement.
  • a computer device including a memory and a processor, the memory stores computer-readable instructions, and the processor implements the following steps when executing the computer-readable instructions: acquiring an image to be recognized; Perform Fourier transform on the image to be recognized to obtain the Fourier spectrum feature map; extract the local binary mode LBP feature value from the Fourier spectrum feature map; generate the LBP histogram representing the statistical probability of the LBP feature value according to the extracted LBP feature value Figure statistical data; input the LBP histogram statistical data into the neural network model, and use the neural network model to identify the LBP histogram statistical data to obtain the first remake probability; when the first remake probability reaches the probability threshold, it is determined that the image to be recognized is Retake an image.
  • the processor further implements the following steps when executing the computer-readable instructions: determining the size of the image to be recognized; when the size of the image to be recognized is larger than the first preset size or smaller than the second preset size, according to The image to be recognized is scaled by the preset standard size; the zoomed image to be recognized is decomposed into three images of RGB channels; the three images are respectively Fourier transformed, and the transformed images are synthesized; the synthesized processing The resulting image is converted into a single-channel Fourier spectrum characteristic map.
  • the processor further implements the following steps when executing the computer-readable instructions: divide the Fourier spectrum feature map into a plurality of sub Fourier spectrum feature blocks; each sub-Fourier spectrum feature block is equally divided into multiple Pixel block; in each pixel block, determine whether the gray value of the non-central pixel is greater than the gray value of the central pixel; if yes, set the gray value of the non-central pixel to the first value; if not, then Set the gray value of the non-central pixel to the second value; perform a weighted summation on the gray value of the non-central pixel in each pixel block after the gray value is set; use the result of the weighted sum as the pixel block LBP characteristic value.
  • the processor further implements the following steps when executing the computer-readable instructions: generating and sending an image review request carrying the image to be identified; receiving a review result in response to the image review request; the review result carrying the image to be identified is a remake The second remake probability of the image; obtain the first weight corresponding to the machine recognition and the second weight corresponding to the review recognition; perform a weighted summation of the first remake probability and the second remake probability according to the first weight and the second weight respectively , To obtain the weighted sum of the reproduction probability; when the weighted sum is greater than or equal to the preset weighted sum, it is finally determined that the image to be recognized is the reproduced image.
  • the processor further implements the following steps when executing the computer-readable instructions: obtain the sample image to be identified, mark the sample image to be identified, and obtain the sample image to be identified containing the label; the label is used to indicate the sample image to be identified Whether it is a remake of the image; Fourier transform the sample image to be identified containing the label to obtain the Fourier spectrum feature sample image; extract the LBP feature sample value from the Fourier spectrum feature sample image; according to the extracted LBP feature value Generate LBP histogram statistical data samples that represent the statistical probability of LBP feature values; input LBP histogram statistical data samples into the neural network model, and use the neural network model to identify the LBP histogram statistical data samples to obtain the third remake probability; Compare the difference between the third remake probability and the label, and adjust the parameters of the neural network model.
  • the processor further implements the following steps when executing the computer-readable instructions: determining the error between the third remake probability and the label; propagating the error back to the network layer of the neural network model to obtain the parameters of each network layer The gradient of; adjust the parameters of each network layer in the neural network model according to the obtained gradient.
  • a computer-readable storage medium is provided, and computer-readable instructions are stored thereon.
  • the following steps are implemented: acquiring an image to be recognized; The image undergoes Fourier transform to obtain the Fourier spectrum feature map; extract the local binary mode LBP feature value from the Fourier spectrum feature map; generate the LBP histogram statistical data representing the statistical probability of the LBP feature value according to the extracted LBP feature value ; Input the LBP histogram statistical data into the neural network model, and identify the LBP histogram statistical data through the neural network model to obtain the first remake probability; when the first remake probability reaches the probability threshold, the image to be identified is determined to be the remake image.
  • the following steps are further implemented: determining the size of the image to be recognized; when the size of the image to be recognized is larger than the first preset size or smaller than the second preset size, Scale the image to be recognized according to the preset standard size; decompose the scaled image to be recognized into three images with RGB channels; perform Fourier transform on the three images respectively, and synthesize the transformed images; The processed image is converted into a single-channel Fourier spectrum characteristic map.
  • the following steps are further implemented: divide the Fourier spectrum feature map into multiple sub Fourier spectrum feature blocks; and divide each sub Fourier spectrum feature block equally into multiple blocks.
  • the following steps are also implemented: generating and sending an image review request carrying the image to be identified; receiving a review result in response to the image review request; the review result carrying the image to be identified is The second remake probability of the remake image; obtain the first weight corresponding to the machine recognition and the second weight corresponding to the review recognition; weight the first remake probability and the second remake probability according to the first weight and the second weight respectively Sum, to obtain the weighted sum of the probabilities of copying; when the weighted sum is greater than or equal to the preset weighted sum, it is finally determined that the image to be recognized is the copying image.
  • the following steps are also implemented: obtaining a sample image to be identified, marking the sample image to be identified, and obtaining a sample image to be identified containing a label; the label is used to indicate the sample to be identified Whether the image is a remake; the Fourier transform is performed on the sample image to be identified containing the label to obtain the Fourier spectrum feature sample image; the LBP feature sample value is extracted from the Fourier spectrum feature sample image; according to the extracted LBP feature Value generation is used to represent the LBP histogram statistical data sample of the statistical probability of the LBP feature value; the LBP histogram statistical data sample is input into the neural network model, and the LBP histogram statistical data sample is identified through the neural network model to obtain the third remake probability ; Compare the difference between the third remake probability and the label, and adjust the parameters of the neural network model.
  • the following steps are also implemented: determine the error between the third remake probability and the label; back-propagate the error to the network layer of the neural network model to obtain each network layer The gradient of the parameter; adjust the parameters of each network layer in the neural network model according to the obtained gradient.
  • Non-volatile memory may 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.
  • RAM random access memory
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain (Synchlink) DRAM
  • RDRAM direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

一种翻拍图像识别方法，包括：获取待识别图像；对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；从所述傅立叶频谱特征图中提取LBP特征值；根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。

Description

翻拍图像识别方法、装置、计算机设备和计算机可读存储介质
相关申请的交叉引用
本申请要求于2019年01月16日提交中国专利局，申请号为2019100400045，申请名称为“翻拍图像识别方法、装置、计算机设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及一种翻拍图像识别方法、装置、计算机设备和计算机可读存储介质。
背景技术
随着计算机技术和互联网技术的不断发展，在网上在线开设账户或办理重要业务，可能需要用户通过移动终端或网络摄像头等设备拍摄并上传相应的图像，如用户的证件图像或用户本人的真实图像。
然而，发明人意识到，上传的图像可能不是用户通过拍摄本人所得的真实图像，或通过拍摄真实的证件所得到的证件图像，而是通过翻拍电脑或手机等设备的显示屏上所展示的图片所得，从而存在伪造用户图像的问题，若不将这些翻拍图像识别出来，将会导致用户信息出现安全性问题。
发明内容
根据本申请公开的各种实施例，提供一种翻拍图像识别方法、装置、计算机设备和计算机可读存储介质。
一种翻拍图像识别方法，由服务器执行，所述方法包括：
获取待识别图像；
对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
一种翻拍图像识别装置，所述装置包括：
图像获取模块，用于获取待识别图像；
变换模块，用于对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
特征值提取模块，用于从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
生成模块，用于根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直 方图统计数据；
处理模块，用于将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
翻拍图像确定模块，用于当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
一种计算机设备，包括存储器和处理器，所述存储器存储有计算机可读指令，所述处理器执行所述计算机可读指令时实现以下步骤：
获取待识别图像；
对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；
当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
一种非易失性计算机可读存储介质，其上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现以下步骤：
获取待识别图像；
对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为根据一个或多个实施例中翻拍图像识别方法的应用场景图。
图2为根据一个或多个实施例中翻拍图像识别方法的流程示意图。
图3为根据一个或多个实施例中提取LBP特征的示意图。
图4为根据另一个或多个实施例中训练神经网络模型的步骤的流程示意图。
图5为根据一个或多个实施例中翻拍图像识别装置的结构框图。
图6为根据另一个或多个实施例中翻拍图像识别装置的结构框图。
图7为根据一个或多个实施例中计算机设备的内部结构图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请提供的翻拍图像识别方法，可以应用于如图1所示的应用环境中。其中，终端102通过网络与服务器104通过网络进行通信。服务器104通过终端102获取待识别图像，对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；服务器104从傅立叶频谱特征图中提取LBP(Local Binary Pattern，局部二值模式)特征值，计算LBP特征值在各特征值范围内的统计概率，将统计概率输入神经网络模型，通过该神经网络模型可以确定待识别图像是否为翻拍图像。其中，终端102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备，服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。
在其中一个实施例中，如图2所示，提供了一种翻拍图像识别方法，以该方法应用于图1中的服务器为例进行说明，包括以下步骤：
S202，获取待识别图像。
待识别图像可以是包括RGB通道的32位彩色图，此外，还可以是包括RGB通道的64位或128位彩色图。
在其中一个实施例中，服务器通过安装在终端的摄像头拍摄待识别图像；或者，服务器通过终端从存储于终端本地相册中获取待识别图像。
在其中一个实施例中，若翻拍图像的识别方法应用于用户注册账户(如注册金融账户)的场景中，在终端展示上传图像的操作页面中可以有两个功能选项，一是支持用户实时拍摄图像的功能选项，另一是支持用户选择从相册中获取图像的功能选项。若用户选择实时拍摄图像的功能选项时，终端则调用摄像头拍摄用户或用户证件的图像，将拍摄的图像作为待识别图像。若用户选择从相册中获取图像的功能选项时，终端则从本地相册中获取用户或用户证件的图像，将获取的图像作为待识别图像。
例如，当用户在网上进行信用卡开户或证券开户时，开户系统将会展示上传用户证件图像和用户本人的真实图像的操作页面。在上传用户本人的真实图像或用户证件照时，可以调用终端的摄像头以便用户进行图像拍摄，也可以通过在操作页面上所提供功能选项从相册选取对应的待识别图像，也可以调用终端的摄像头以便用户进行图像拍摄。
S204，对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图。
经过傅里叶变换后所得到的傅立叶频谱特征图可以是单通道8位、或16位、或32位的灰度图，该频谱特征图反映了频谱能量点的聚集情况。傅里叶变换可以是离散傅里叶变换。
在其中一个实施例中，S204具体可以包括：确定待识别图像的尺寸；当待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对待识别图像进行缩放； 将缩放后的待识别图像分解为RGB通道的三幅图像；分别对三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
例如，对获取的图像进行傅里叶变换之前，终端先确定待识别图像的大小，如图像分辨率或尺寸。若待识别图像的大小过大或过小，则将图像放大或压缩到预设的标准大小。终端将缩放后的图像按照RGB三通道分解为对应的三幅图像，对每一副图像进行傅里叶变换，然后将变化后的图像进行合成，得到组合特征图。终端对这个组合特征图进行灰度转换，从而可以得到单通道的傅里叶频谱特征图。上述实施例中，对过大的图像进行压缩，可以降低识别过程中的计算量。
S206，从傅立叶频谱特征图中提取局部二值模式LBP特征值。
在其中一个实施例中，服务器将傅立叶频谱特征图均匀划分为多个子傅立叶频谱特征图块，在每个子傅立叶频谱特征图块的像素点提取LBP特征值。
具体地，服务器将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块，分别对各子傅立叶频谱特征图块均分为多个像素块。服务器在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；若是，则将非中心像素点的灰度值设置为第一数值；若否，则将非中心像素点的灰度值设置为第二数值。服务器对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和，将加权求和的结果作为各像素块的LBP特征值。
例如，如图3所示，对于子傅立叶频谱特征图块中的某个像素点，服务器以该像素点为中心像素点，将该中心像素点的灰度值分别与3×3窗口中各领域像素点的灰度值进行比较，若领域像素点的灰度值大于或等于中心像素点的灰度值，则将领域像素点的灰度值设置为1；若领域像素点的灰度值小于中心像素点的灰度值，则将领域像素点的灰度值设置为0，因此计算出该中心像素点的灰度值的二进制为01111100，转换为十进制为124。通过上述方法，可以得到各子傅立叶频谱特征图块的LBP特征值。
S208，根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据。
在其中一个实施例中，服务器可以对各子傅立叶频谱特征图块中的LBP特征值，按照大小顺序进行排序；将排序后各子傅立叶频谱特征图块中的LBP特征值，按照预设步长均匀分成多个特征值范围；计算归属于各特征值范围内LBP特征值的统计概率。
在其中一个实施例中，在每个子傅立叶频谱特征图块中，根据各特征值范围内LBP特征值的统计概率建立LBP直方图，将每个LBP直方图对应的概率分别保存于不同的子数组A1、A2…An中，得到各子傅立叶频谱特征图块的LBP特征值在各预设特征值范围内的统计概率，该统计概率可以是LBP直方图统计数据。在输入神经网络模型之前，服务器将子数组A1、A2…An保存于另一个数组S中。
S210，将LBP直方图统计数据输入神经网络模型，通过神经网络模型对LBP直方图统计数据进行识别处理，获得第一翻拍概率。
在其中一个实施例中，服务器以数组的形式，将LBP直方图统计数据输入训练好的神经网络模型。
S212，当第一翻拍概率达到概率阈值时，确定待识别图像为翻拍图像。
例如，假设概率阈值为90％，当第一翻拍概率大于或等于90％时，则待识别图像为翻拍图像。当第一翻拍概率小于90％时，则待识别图像不是翻拍图像。
在其中一个实施例中，为了进一步保证识别结果的准确性，S212之后，该方法还包括：服务器生成携带有待识别图像的图像复核请求；将图像复核请求发送至复核人账号，以使复核人对所述待识别图像进行复核并反馈复核结果；接收到复核人账号反馈的、针对图像复核请求的复核结果；复核结果携带有待识别图像为翻拍图像的第二翻拍概率；获取机器识别所对应的第一权重和复核识别所对应的第二权重；分别按照第一权重和第二权重，对第一翻拍概率和第二翻拍概率进行加权求和，得到翻拍概率的加权和；当加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
第一权重和第二权重为不同的两个值，第一权重与第二权重的和值为1。
例如，为了进一步保证识别结果的准确性，当机器识别该待识别图像为翻拍图像时，还会将待识别图像发送给专业人员，由专业人员对待识别图像进行进一步复核识别，然后反馈包含识别结果的复核结果，该识别结果可以是待识别图像为翻拍图像的概率。若待识别图像为翻拍图像的概率很大(如大于90％)时，则最终确定待识别图像为翻拍图像。若待识别图像为翻拍图像的概率较小(如小于90％)时，则将两个概率进行加权求和，得到翻拍概率的加权和，如加权和为p＝k 1×p 1+k 2×p 2(k 1和k 2分别为第一权重和第二权重，p 1和p 2分别为第一翻拍概率和第二翻拍概率)，根据加权和来确定待识别图像是否为翻拍图像。当加权和p大于或等于预设加权和时，最终确定待识别图像为翻拍图像。当加权和p小于预设加权和时，最终确定待识别图像不是翻拍图像。
在其中一个实施例中，服务器可以在结合复核识别的结果确定待识别图像是否为翻拍图像。当第一翻拍概率达到概率阈值时，服务器也可以不需要再进行复核识别的过程，直接确定待识别图像为翻拍图像。
在其中一个实施例中，该方法可应用于金融开户。在最终确定待识别图像为翻拍图像时，服务器拒绝用户的开户请求；在确定待识别图像为非翻拍图像时，服务器则进一步审核用户所提交的其它材料，若都满足开户要求，则允许用户的开户请求。
作为一个示例，(1)首先将将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块，然后将子傅立叶频谱特征图块分为多个像素块；(2)在每个像素块中，将中心像素点的灰度值与相邻的8个像素点的灰度值进行比较，若8个像素点中任一个像素点的灰度值大于中心像素点的灰度值，则将对应像素点的灰度值设置为1，否则设置为0。这样，3×3窗口邻域内的8个像素点可产生8位二进制数，即得到该窗口中心像素点的LBP特征值；(3)然后计算每个像素块的统计直方图，该统计直方图用于表示各预设特征值范围内LBP特征值出现的频率，即所述的统计概率；其中，还可以对该直方图进行归一化处理。(4)最后根据所得到的每个像素块的统计直方图，得到整幅待识别图像的LBP纹理特征向量，将LBP纹理特征向量输入神经网络模型，通过神经网络的处理可以得到待识别图像是否为翻拍图像。
上述实施例中，根据获取的待识别图像生成傅立叶频谱特征图，从傅立叶频谱特征图中 提取LBP特征值，从而得到待识别图像的局部纹理特征。生成用于表示LBP特征值统计概率的LBP直方图统计数据，将该LBP直方图统计数据输入神经网络模型，通过神经网络模型可以确定待识别图像是否为翻拍图像，从而避免待识别图像为翻拍电脑或手机等设备的显示屏上所展示的图片所得，从而确保图像的真实性，避免了第三方使用翻拍图像导致用户信息出现安全隐患。此外，通过上述实施例，可以有效地提高翻拍图像识别的准确率，且准确率由原来的80％提高至93％。
在其中一个实施例中，如图4所示，该方法还包括：
S402，获取待识别样本图，对待识别样本图进行标注，获得包含标签的待识别样本图；标签用于表示待识别样本图是否为翻拍图像。
待识别样本图可以是包括RGB通道的32位彩色图，此外，还可以是包括RGB通道的64位或128位彩色图。待识别样本图的尺寸为预设标准尺寸。
S404，对包含标签的待识别样本图进行傅里叶变换，获得傅里叶频谱特征样本图。
在其中一个实施例中，S404之前，该方法还可以包括：服务器对包含标签的待识别样本图进行0到360度间的随机旋转，以及随机缩放、调整亮度、色度和清晰度等处理，然后根据处理后的待识别样本图生成对应的傅里叶频谱特征样本图，以增加神经网络模型的泛化能力，从而提高翻拍识别的准确率。
S406，从傅里叶频谱特征样本图中提取LBP特征样本值。
在其中一个实施例中，将傅里叶频谱特征样本图均匀划分为多个子傅里叶频谱特征样本图块，在每个子傅里叶频谱特征样本图块的像素提取LBP特征样本值。
具体地，将傅里叶频谱特征样本图均分为多个子傅里叶频谱特征样本图块；分别对各子傅里叶频谱特征样本图块均分为多个样本像素块；在各样本像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；若是，则将非中心像素点的灰度值设置为第一数值；若否，则将非中心像素点的灰度值设置为第二数值；对设置灰度值后各样本像素块中的非中心像素点的灰度值进行加权求和；将加权求和的结果作为各样本像素块的LBP特征样本值。
例如，如图3所示，对于子傅里叶频谱特征样本图块中的某个像素点，以该像素点为中心像素点，将该中心像素点的灰度值分别与3×3窗口中各领域像素点的灰度值进行比较，若领域像素点的灰度值大于或等于中心像素点的灰度值，则将领域像素点的灰度值设置为1；若领域像素点的灰度值小于中心像素点的灰度值，则将领域像素点的灰度值设置为0，因此计算出该中心像素点的灰度值的二进制为01111100，转换为十进制为124，因此可以得到各子傅里叶频谱特征样本图块的LBP特征样本值。
S408，根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据样本。
在其中一个实施例中，S208具体可以包括：服务器可以对各子傅里叶频谱特征样本图块中的LBP特征样本值，按照大小顺序进行排序；将排序后各子傅里叶频谱特征样本图块中的LBP特征样本值，按照预设步长均匀分成多个特征值范围；计算归属于各特征值范围内LBP特征样本值的统计概率。
在其中一个实施例中，在每个子傅里叶频谱特征样本图块中，根据各特征值范围内LBP特征值的统计概率建立LBP直方图，将每个LBP直方图对应的概率分别保存于不同的子数组A1、A2…An中，得到各子傅里叶频谱特征样本图块的LBP特征样本值在各预设特征值范围内的统计概率。在输入神经网络模型之前，服务器将子数组A1、A2…An保存于一个大的数组S中。
S410，将LBP直方图统计数据样本输入神经网络模型，通过神经网络模型对LBP直方图统计数据样本进行识别处理，获得第三翻拍概率。
在其中一个实施例中，服务器以数组的形式，将LBP直方图统计数据样本输入训练好的神经网络模型。
S412，对比第三翻拍概率与标签之间的差异，调整神经网络模型的参数。
在其中一个实施例中，S412包括：确定第三翻拍概率与标签之间的误差；将误差反向传播到神经网络模型的网络层，获得各网络层参数的梯度；根据所获得的梯度调整神经网络模型中各网络层的参数。
服务器可以通过损失函数确定第三翻拍概率与标签之间的误差。损失函数可以是以下任一种：均方误差(Mean Squared Error)、交叉熵损失函数、L2Loss函数和Focal Loss函数。
应该理解的是，虽然图2、4的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2、4中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
应该理解的是，虽然图2、4的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2、4中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在其中一个实施例中，如图5所示，提供了一种翻拍图像识别装置，包括：图像获取模块502、变换模块504、特征值提取模块506、生成模块508、处理模块510和翻拍图像确定模块512，其中：
图像获取模块502，用于获取待识别图像；
变换模块504，用于对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
特征值提取模块506，用于从傅立叶频谱特征图中提取局部二值模式LBP特征值；
生成模块508，用于根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
处理模块510，用于将LBP直方图统计数据输入神经网络模型，通过神经网络模型对LBP直方图统计数据进行识别处理，获得第一翻拍概率；
翻拍图像确定模块512，用于当第一翻拍概率达到概率阈值时，确定待识别图像为翻拍图像。
在其中一个实施例中，变换模块504还用于：确定待识别图像的尺寸；当待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对待识别图像进行缩放；将缩放后的待识别图像分解为RGB通道的三幅图像；分别对三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
在其中一个实施例中，特征值提取模块506还用于：将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；分别对各子傅立叶频谱特征图块均分为多个像素块；在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；若是，则将非中心像素点的灰度值设置为第一数值；若否，则将非中心像素点的灰度值设置为第二数值；对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；将加权求和的结果作为各像素块的LBP特征值。
在其中一个实施例中，如图6所示，装置还包括：请求生成模块514、发送模块516、接收模块518、权重获取模块520和加权模块522，其中：
请求生成模块514，用于生成携带有待识别图像的图像复核请求；
发送模块516，用于发送图像复核请求；
接收模块518，用于接收响应于图像复核请求的复核结果；复核结果携带有待识别图像为翻拍图像的第二翻拍概率；
权重获取模块520，用于获取机器识别所对应的第一权重和复核识别所对应的第二权重；
加权模块522，分别按照第一权重和第二权重，对第一翻拍概率和第二翻拍概率进行加权求和，得到翻拍概率的加权和；
翻拍图像确定模块512还用于当加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
在其中一个实施例中，如图6所示，装置还包括：调整模块524，其中：
图像获取模块502还用于图像获取模块还用于获取待识别样本图，对待识别样本图进行标注，获得包含标签的待识别样本图；标签用于表示待识别样本图是否为翻拍图像；
变换模块504还用于对包含标签的待识别样本图进行傅里叶变换，获得傅里叶频谱特征样本图；
特征值提取模504块还用于从傅里叶频谱特征样本图中提取LBP特征样本值；
生成模块508还用于根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据样本；
处理模块510还用于将LBP直方图统计数据样本输入神经网络模型，通过神经网络模型对LBP直方图统计数据样本进行识别处理，获得第三翻拍概率；
调整模块524，用于对比第三翻拍概率与标签之间的差异，调整神经网络模型的参数。
在其中一个实施例中，调整模块524还用于：确定第三翻拍概率与标签之间的误差；将误差反向传播到神经网络模型的网络层，获得各网络层参数的梯度；根据所获得的梯度调整神经网络模型中各网络层的参数。
上述实施例中，根据获取的待识别图像生成傅立叶频谱特征图，从傅立叶频谱特征图中提取LBP特征值，从而得到待识别图像的局部纹理特征。生成用于表示LBP特征值统计概率的LBP直方图统计数据，将该LBP直方图统计数据输入神经网络模型，通过神经网络模型可以确定待识别图像是否为翻拍图像，从而避免待识别图像为翻拍电脑或手机等设备的显示屏上所展示的图片所得，从而确保图像的真实性，避免了第三方使用翻拍图像导致用户信息出现安全隐患。此外，通过上述实施例，可以有效地提高翻拍图像识别的准确率，且准确率由原来的80％提高至93％。
关于翻拍图像识别装置的具体限定可以参见上文中对于翻拍图像识别方法的限定，在此不再赘述。上述翻拍图像识别装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在其中一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器，其内部结构图可以如图7所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口和数据库。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统、计算机可读指令和数据库。该内存储器为非易失性存储介质中的操作系统和计算机可读指令的运行提供环境。该计算机设备的数据库用于存储待识别图像和待识别样本图等数据。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机可读指令被处理器执行时以实现一种翻拍图像识别方法。
本领域技术人员可以理解，图7中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在其中一个实施例中，提供了一种计算机设备，包括存储器和处理器，该存储器存储有计算机可读指令，该处理器执行计算机可读指令时实现以下步骤：获取待识别图像；对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；从傅立叶频谱特征图中提取局部二值模式LBP特征值；根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；将LBP直方图统计数据输入神经网络模型，通过神经网络模型对LBP直方图统计数据进行识别处理，获得第一翻拍概率；当第一翻拍概率达到概率阈值时，确定待识别图像为翻拍图像。
在其中一个实施例中，处理器执行计算机可读指令时还实现以下步骤：确定待识别图像的尺寸；当待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺 寸对待识别图像进行缩放；将缩放后的待识别图像分解为RGB通道的三幅图像；分别对三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
在其中一个实施例中，处理器执行计算机可读指令时还实现以下步骤：将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；分别对各子傅立叶频谱特征图块均分为多个像素块；在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；若是，则将非中心像素点的灰度值设置为第一数值；若否，则将非中心像素点的灰度值设置为第二数值；对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；将加权求和的结果作为各像素块的LBP特征值。
在其中一个实施例中，处理器执行计算机可读指令时还实现以下步骤：生成并发送携带有待识别图像的图像复核请求；接收响应于图像复核请求的复核结果；复核结果携带有待识别图像为翻拍图像的第二翻拍概率；获取机器识别所对应的第一权重和复核识别所对应的第二权重；分别按照第一权重和第二权重，对第一翻拍概率和第二翻拍概率进行加权求和，得到翻拍概率的加权和；当加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
在其中一个实施例中，处理器执行计算机可读指令时还实现以下步骤：获取待识别样本图，对待识别样本图进行标注，获得包含标签的待识别样本图；标签用于表示待识别样本图是否为翻拍图像；对包含标签的待识别样本图进行傅里叶变换，获得傅里叶频谱特征样本图；从傅里叶频谱特征样本图中提取LBP特征样本值；根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据样本；将LBP直方图统计数据样本输入神经网络模型，通过神经网络模型对LBP直方图统计数据样本进行识别处理，获得第三翻拍概率；对比第三翻拍概率与标签之间的差异，调整神经网络模型的参数。
在其中一个实施例中，处理器执行计算机可读指令时还实现以下步骤：确定第三翻拍概率与标签之间的误差；将误差反向传播到神经网络模型的网络层，获得各网络层参数的梯度；根据所获得的梯度调整神经网络模型中各网络层的参数。
在其中一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机可读指令，计算机可读指令被处理器执行时实现以下步骤：获取待识别图像；对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；从傅立叶频谱特征图中提取局部二值模式LBP特征值；根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；将LBP直方图统计数据输入神经网络模型，通过神经网络模型对LBP直方图统计数据进行识别处理，获得第一翻拍概率；当第一翻拍概率达到概率阈值时，确定待识别图像为翻拍图像。
在其中一个实施例中，计算机可读指令被处理器执行时还实现以下步骤：确定待识别图像的尺寸；当待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对待识别图像进行缩放；将缩放后的待识别图像分解为RGB通道的三幅图像；分别对三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；将合成处理所得的图像转换为 单通道的傅立叶频谱特征图。
在其中一个实施例中，计算机可读指令被处理器执行时还实现以下步骤：将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；分别对各子傅立叶频谱特征图块均分为多个像素块；在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；若是，则将非中心像素点的灰度值设置为第一数值；若否，则将非中心像素点的灰度值设置为第二数值；对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；将加权求和的结果作为各像素块的LBP特征值。
在其中一个实施例中，计算机可读指令被处理器执行时还实现以下步骤：生成并发送携带有待识别图像的图像复核请求；接收响应于图像复核请求的复核结果；复核结果携带有待识别图像为翻拍图像的第二翻拍概率；获取机器识别所对应的第一权重和复核识别所对应的第二权重；分别按照第一权重和第二权重，对第一翻拍概率和第二翻拍概率进行加权求和，得到翻拍概率的加权和；当加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
在其中一个实施例中，计算机可读指令被处理器执行时还实现以下步骤：获取待识别样本图，对待识别样本图进行标注，获得包含标签的待识别样本图；标签用于表示待识别样本图是否为翻拍图像；对包含标签的待识别样本图进行傅里叶变换，获得傅里叶频谱特征样本图；从傅里叶频谱特征样本图中提取LBP特征样本值；根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据样本；将LBP直方图统计数据样本输入神经网络模型，通过神经网络模型对LBP直方图统计数据样本进行识别处理，获得第三翻拍概率；对比第三翻拍概率与标签之间的差异，调整神经网络模型的参数。
在其中一个实施例中，计算机可读指令被处理器执行时还实现以下步骤：确定第三翻拍概率与标签之间的误差；将误差反向传播到神经网络模型的网络层，获得各网络层参数的梯度；根据所获得的梯度调整神经网络模型中各网络层的参数。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一非易失性计算机可读取存储介质中，该计算机可读指令在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各 个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种翻拍图像识别方法，由服务器执行，所述方法包括：
    获取待识别图像；
    对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
    从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
    根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
    将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
    当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
  2. 根据权利要求1所述的方法，其特征在于，所述对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图包括：
    确定所述待识别图像的尺寸；
    当所述待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对所述待识别图像进行缩放；
    将缩放后的待识别图像分解为RGB通道的三幅图像；
    分别对所述三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；及
    将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
  3. 根据权利要求1所述的方法，其特征在于，所述从傅立叶频谱特征图中提取局部二值模式LBP特征值包括：
    将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；
    分别对各所述子傅立叶频谱特征图块均分为多个像素块；
    在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；
    若是，则将所述非中心像素点的灰度值设置为第一数值；若否，则将所述非中心像素点的灰度值设置为第二数值；
    对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；及
    将加权求和的结果作为各像素块的LBP特征值。
  4. 根据权利要求1至3任一项所述的方法，其特征在于，所述确定所述待识别图像为翻拍图像之后，所述方法还包括：
    生成并发送携带有所述待识别图像的图像复核请求；
    接收响应于所述图像复核请求的复核结果；所述复核结果携带有所述待识别图像为翻拍图像的第二翻拍概率；
    获取机器识别所对应的第一权重和复核识别所对应的第二权重；
    分别按照所述第一权重和所述第二权重，对第一翻拍概率和所述第二翻拍概率进行加权求和，得到翻拍概率的加权和；及
    当所述加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
  5. 根据权利要求1至3任一项所述的方法，其特征在于，所述方法还包括：
    获取待识别样本图，对所述待识别样本图进行标注，获得包含标签的待识别样本图；所述标签用于表示所述待识别样本图是否为翻拍图像；
    对包含所述标签的待识别样本图进行傅里叶变换，获得傅里叶频谱特征样本图；
    从所述傅里叶频谱特征样本图中提取LBP特征样本值；
    根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据样本；
    将所述LBP直方图统计数据样本输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据样本进行识别处理，获得第三翻拍概率；及
    对比所述第三翻拍概率与所述标签之间的差异，调整神经网络模型的参数。
  6. 根据权利要求5所述的方法，其特征在于，所述对比所述第三翻拍概率与所述标签之间的差异，调整神经网络模型的参数包括：
    确定所述第三翻拍概率与所述标签之间的误差；
    将所述误差反向传播到神经网络模型的网络层，获得各网络层参数的梯度；及
    根据所获得的梯度调整所述神经网络模型中各网络层的参数。
  7. 根据权利要求1所述的方法，其特征在于，所述从傅立叶频谱特征图中提取局部二值模式LBP特征值包括：
    将傅立叶频谱特征图均匀划分为多个子傅立叶频谱特征图块；及
    在每个所述子傅立叶频谱特征图块的像素点提取局部二值模式LBP特征值。
  8. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    对各所述子傅立叶频谱特征图块中的LBP特征值，按照大小顺序进行排序；
    将排序后的各所述子傅立叶频谱特征图块中的LBP特征值，按照预设步长均匀分成多个特征值范围；及
    计算归属于各所述特征值范围内的LBP特征值统计概率。
  9. 一种翻拍图像识别装置，其特征在于，所述装置包括：
    图像获取模块，用于获取待识别图像；
    变换模块，用于对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
    特征值提取模块，用于从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
    生成模块，用于根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
    处理模块，用于将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；
    翻拍图像确定模块，用于当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
  10. 根据权利要求9所述的装置，其特征在于，所述变换模块还用于：
    确定所述待识别图像的尺寸；
    当所述待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺 寸对所述待识别图像进行缩放；
    将缩放后的待识别图像分解为RGB通道的三幅图像；
    分别对所述三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；
    将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
  11. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机可读指令，
    所述计算机可读指令被处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取待识别图像；
    对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
    从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
    根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
    将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
    当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
  12. 根据权利要求11所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    确定所述待识别图像的尺寸；
    当所述待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对所述待识别图像进行缩放；
    将缩放后的待识别图像分解为RGB通道的三幅图像；
    分别对所述三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；及
    将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
  13. 根据权利要求11所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；
    分别对各所述子傅立叶频谱特征图块均分为多个像素块；
    在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；
    若是，则将所述非中心像素点的灰度值设置为第一数值；若否，则将所述非中心像素点的灰度值设置为第二数值；
    对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；及
    将加权求和的结果作为各像素块的LBP特征值。
  14. 根据权利要求11至13任一项所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    生成并发送携带有所述待识别图像的图像复核请求；
    接收响应于所述图像复核请求的复核结果；所述复核结果携带有所述待识别图像为翻拍图像的第二翻拍概率；
    获取机器识别所对应的第一权重和复核识别所对应的第二权重；
    分别按照所述第一权重和所述第二权重，对第一翻拍概率和所述第二翻拍概率进行加权求和，得到翻拍概率的加权和；及
    当所述加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
  15. 根据权利要求11所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    对各所述子傅立叶频谱特征图块中的LBP特征值，按照大小顺序进行排序；
    将排序后的各所述子傅立叶频谱特征图块中的LBP特征值，按照预设步长均匀分成多个特征值范围；及
    计算归属于各所述特征值范围内的LBP特征值统计概率。
  16. 一种非易失性计算机可读存储介质，其上存储有计算机可读指令，所述计算机可读指令被处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取待识别图像；
    对所获取的待识别图像进行傅里叶变换，获得傅立叶频谱特征图；
    从所述傅立叶频谱特征图中提取局部二值模式LBP特征值；
    根据所提取的LBP特征值生成用于表示LBP特征值统计概率的LBP直方图统计数据；
    将所述LBP直方图统计数据输入神经网络模型，通过所述神经网络模型对所述LBP直方图统计数据进行识别处理，获得第一翻拍概率；及
    当所述第一翻拍概率达到概率阈值时，确定所述待识别图像为翻拍图像。
  17. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：：
    确定所述待识别图像的尺寸；
    当所述待识别图像的尺寸大于第一预设尺寸，或小于第二预设尺寸时，按照预设标准尺寸对所述待识别图像进行缩放；
    将缩放后的待识别图像分解为RGB通道的三幅图像；
    分别对所述三幅图像进行傅里叶变换，将变换后所得的图像进行合成处理；及
    将合成处理所得的图像转换为单通道的傅立叶频谱特征图。
  18. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：：
    将傅立叶频谱特征图均分为多个子傅立叶频谱特征图块；
    分别对各所述子傅立叶频谱特征图块均分为多个像素块；
    在各像素块中，判断非中心像素点的灰度值是否大于中心像素点的灰度值；
    若是，则将所述非中心像素点的灰度值设置为第一数值；若否，则将所述非中心像素点的灰度值设置为第二数值；
    对设置灰度值后各像素块中的非中心像素点的灰度值进行加权求和；及
    将加权求和的结果作为各像素块的LBP特征值。
  19. 根据权利要求16至18任一项所述的存储介质，其特征在于，所述计算机可读指令 被所述处理器执行时还执行以下步骤：
    生成并发送携带有所述待识别图像的图像复核请求；
    接收响应于所述图像复核请求的复核结果；所述复核结果携带有所述待识别图像为翻拍图像的第二翻拍概率；
    获取机器识别所对应的第一权重和复核识别所对应的第二权重；
    分别按照所述第一权重和所述第二权重，对第一翻拍概率和所述第二翻拍概率进行加权求和，得到翻拍概率的加权和；及
    当所述加权和大于或等于预设加权和时，最终确定待识别图像为翻拍图像。
  20. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：：
    对各所述子傅立叶频谱特征图块中的LBP特征值，按照大小顺序进行排序；
    将排序后的各所述子傅立叶频谱特征图块中的LBP特征值，按照预设步长均匀分成多个特征值范围；及
    计算归属于各所述特征值范围内的LBP特征值统计概率。
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