CN111368944A

CN111368944A - Method and device for recognizing copied image and certificate photo and training model and electronic equipment

Info

Publication number: CN111368944A
Application number: CN202010460740.9A
Authority: CN
Inventors: 徐炎; 郭明宇; 陈弢
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2020-05-27
Filing date: 2020-05-27
Publication date: 2020-07-03
Anticipated expiration: 2040-05-27
Also published as: CN111368944B

Abstract

The embodiment of the specification discloses a method, a device and an electronic device for recognizing a copied image and a certificate photo and training a model.

Description

Method and device for recognizing copied image and certificate photo and training model and electronic equipment

Technical Field

The embodiment of the specification relates to the technical field of computers, in particular to a method and a device for recognizing a copied image and a certificate photo and training a model and electronic equipment.

Background

With the development of the technology, the image recognition technology is rapidly developed and widely applied in an explosion mode. But there are also cases where the use of a reproduced image avoids image recognition. Wherein, the copying image is also called copying, which refers to the process of copying files by adopting a photographic method, and comprises the following steps: an image obtained by displaying an image or a video on a display device using an imaging device (e.g., a camera, a video camera, a terminal device having an image capturing or video recording function, etc.).

Therefore, how to recognize the captured image is a topic considered in the industry.

Disclosure of Invention

In view of this, embodiments of the present specification provide a method, an apparatus, and an electronic device for recognizing a copy image and a certificate photo and training a model.

The embodiment of the specification adopts the following technical scheme:

an embodiment of the present specification provides a method for recognizing a copied image, including:

identifying a target object in a first image and a second image respectively, wherein the first image and the second image are acquired under different acquisition conditions of the target object;

performing image fusion on the first image and the second image based on the identified target object;

and judging whether the first image and the second image are the copied images or not by using the Moire pattern change information in the fused image.

An embodiment of the present specification further provides a method for recognizing a copied photo, including:

identifying a target certificate in a first certificate photo and a second certificate photo respectively, wherein the first image and the second image are acquired under different acquisition conditions;

performing image fusion on the first certificate photo and the second certificate photo based on the identified target certificate;

and judging whether the first certificate photo and the second certificate photo are the reproduction certificate photo or not by using the Moire pattern change information in the fused image.

An embodiment of the present specification further provides a model training method, including:

acquiring a fused image sample, wherein the fused image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions;

and training a model by using the fused image sample so as to identify the moire variation information from the fused image by using the model, and judging whether the first image and the second image forming the fused image are the copied image or not by using the moire variation information.

An embodiment of the present specification further provides a method for recognizing a captured image, including:

identifying a target object in a first image and a second image, respectively, the first image and the second image being acquired under different acquisition conditions;

identifying moire variation information from a fusion image by using a model, and judging whether the first image and the second image are copied images or not by using the moire variation information, wherein the model is obtained by training a fusion image sample, the fusion image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions.

An embodiment of the present specification further provides a device for recognizing a captured image, including:

the identification module is used for respectively identifying a target object in a first image and a second image, wherein the first image and the second image are acquired under different acquisition conditions;

the image fusion module is used for carrying out image fusion on the first image and the second image based on the identified target object;

and the judging module is used for judging whether the first image and the second image are the copied images or not by utilizing the Moire pattern change information in the fused image.

An embodiment of the present specification further provides a device for recognizing a copied document photo, including:

the identification module is used for respectively identifying the target certificate in the first certificate photo and the second certificate photo, and the first image and the second image are acquired under different acquisition conditions;

the image fusion module is used for carrying out image fusion on the first certificate photo and the second certificate photo based on the identified target certificate;

and the judging module judges whether the first certificate photo and the second certificate photo are the reproduction certificate photo by utilizing the Moire variation information in the fusion image.

An embodiment of the present specification further provides a model training apparatus, including:

the acquisition module is used for acquiring a fused image sample, wherein the fused image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions;

and the training module is used for training a model by using the fused image sample so as to identify the moire variation information from the fused image by using the model, and judging whether the first image and the second image forming the fused image are the copied image or not by using the moire variation information.

the judgment module is used for identifying moire variation information from a fusion image by using a model, and judging whether the first image and the second image are the copied images or not by using the moire variation information, wherein the model is obtained by training a fusion image sample, the fusion image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions.

An embodiment of the present specification further provides an electronic device, including:

a processor; and

a memory configured to store a computer program that, when executed, causes the processor to:

a processor; and

The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:

and performing image fusion on the first image and the second image acquired under different acquisition conditions based on the identified target object, and judging whether the first image and the second image are the copied images according to the moire variation information in the fused image.

Because the acquisition conditions of the first image and the second image are different, the moire fringes presented by the first image and the second image are different, and the moire variation information represented in the fused image is enhanced, so that the moire characteristics in the fused image are more obvious. This can promote the recognition accuracy to the moire pattern characteristic, further promotes the recognition accuracy to the image of shooing.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the specification and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the specification and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a flowchart of a method for recognizing a copied image according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a model training method proposed in the embodiments of the present disclosure;

fig. 3 is a flowchart of a method for recognizing a copied image according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of a method for recognizing a copied photo according to an embodiment of the present disclosure;

fig. 5 is a flowchart of an application example of a method for recognizing a copied photo according to an embodiment of the present disclosure;

fig. 6 is a structural diagram of a copied image recognition apparatus according to an embodiment of the present disclosure;

FIG. 7 is a block diagram of a device for recognizing a copied photograph in accordance with an embodiment of the present disclosure;

FIG. 8 is a block diagram of a model training apparatus according to an embodiment of the present disclosure;

fig. 9 is a structural diagram of a copied image recognition apparatus according to an embodiment of the present disclosure;

fig. 10 is a schematic diagram illustrating a more specific hardware structure of a computing device according to an embodiment of the present disclosure.

Detailed Description

The prior art is analyzed and found that the current scheme for recognizing the reproduction is generally realized based on image characteristics such as a frame or reflection. Specifically, a commonly used duplication detection algorithm is generally modeled by a classification algorithm in machine learning, namely, a classification method is adopted to classify certificates into normal certificates and duplicated certificates, and then a data-driven mode is adopted to perform model training, so that the purpose of duplication detection of the certificates is achieved.

The embodiment of the specification provides a method, a device and an electronic device for recognizing a copied image and a certificate photo and training a model.

In order to make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present specification and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present application.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a method for recognizing a copied image according to an embodiment of the present disclosure. The execution subject of the method may be a client or a server corresponding to the client, and is not limited in particular here.

Step 101: the target object is identified in a first image and a second image, respectively, which are acquired under different acquisition conditions.

In embodiments of the present description, the first image and the second image may be directly obtained from a database.

In another embodiment of the present description, the first image and the second image may be acquired based on different acquisition conditions in the current scene.

Specifically, before step 101 is executed, the first image and the second image are sequentially acquired based on the different acquisition conditions according to an image acquisition instruction for the target object. In this application scenario, the user may place the target object within the acquisition range of the acquisition device, and the target object is acquired by the acquisition device. For example, a client installed in a terminal device of a user is used to sequentially capture a first image and a second image.

The image capturing device may be generated by a user operation, or may be automatically generated by the capturing device, which is not specifically limited herein.

In other embodiments of this specification, before performing step 101, the method may further include:

sending image acquisition prompt information aiming at the target object to prompt a user to acquire images of the target object based on different acquisition conditions; receiving the uploaded first image and second image.

In this application scenario, a user acquires a first image and a second image by using a terminal device in his/her own, and uploads the first image and the second image, where the first image and the second image may be acquired by the user in advance. For example, the uploaded first image and second image are received with the terminal device.

The image acquisition prompt information can be sound or visual information.

In the embodiments of the present disclosure, the capturing conditions may include a shooting angle, whether a flash is used, a placement angle of a light or a target image, or other capturing conditions, and are not particularly limited herein.

In the embodiment of the present specification, the first image and the second image are two-frame images obtained by photographing or scanning the target object. The target object is identified in the first image and the second image with the purpose of determining position information of the target object in the first image and the second image.

The target object described in the embodiments of the present specification may be a certificate photo or other type of image, and is not particularly limited herein. Depending on the specific type of target object, the target object may be identified in the first image and the second image, respectively, using pixel value differences in the first image and the second image. The target object may be identified in the first image and the second image by using a target object identification model, where the target object identification model may be obtained by using machine learning training, and a specific type of the model is not specifically limited herein.

In addition, the target object in the first image and the second image may be pre-marked, and identifying the target object may refer to determining a position of the target object according to the pre-marking.

Step 103: and performing image fusion on the first image and the second image based on the identified target object.

In the embodiment of the present specification, the first image and the second image are subjected to image fusion based on the identified target object, and specifically, the first image and the second image may be subjected to image fusion based on the identified target object.

Image Fusion (Image Fusion) is a process of synthesizing two or more images into a new Image by using a specific algorithm. The fusion result can utilize the correlation of two (or more) images on time and space and the complementarity of information, and the image obtained after fusion can be more comprehensively and clearly described for the scene. In particular, in embodiments of the present description, this may enhance moir é change information features.

In this embodiment, performing image fusion on the first image and the second image based on the identified target object may include:

performing outer contour registration on the target object identified in the second image by using the target object identified in the first image;

and performing image fusion on the first image and the second image based on the registered target object.

Therefore, when the first image and the second image are registered through the outer contour of the target object, the size and bit width of the target object are kept consistent, and in the fused image, the tampered contents in the first image and the second image cannot be overlapped, especially the moire patterns of the two images cannot be overlapped, and the moire pattern change characteristic is more remarkable due to the fact that the moire patterns are not overlapped. This further makes the identification of subsequent flips more accurate.

In one embodiment, performing outer contour registration on the target object identified in the second image by using the target object identified in the first image may include:

performing reference point detection on the target object identified in the first image;

performing reference point detection on the target object identified in the second image;

projecting the second image to the first image by projecting a reference point in the second image to a reference point in the first image to obtain a second image projection image.

Through projection, the size and bit width of the second image can be adjusted, so that the size and bit width of the second image projection image and the first image are kept consistent.

In this case, the first image and the second image projection image are subjected to image fusion to obtain a fused image of the first image and the second image.

In another embodiment, an outer contour reference line may be drawn for the target object in the first and second images for registration, projection, and fusion through the reference line.

Specifically, the image fusion of the first image and the second image based on the identified target object may be: and splicing the first image and the second image based on the identified target object.

Step 105: and judging whether the first image and the second image are the copied images or not by using the Moire pattern change information in the fused image.

Because the collecting device, such as a camera photosensitive element, has a spatial frequency, and the shot object, such as an image on a liquid crystal screen, has a spatial distribution rule when being presented by liquid crystal pixel points of the liquid crystal screen. When the spatial frequency of the photosensitive element of the camera is close to the spatial distribution rule of the image presented by the liquid crystal screen, the beat phenomenon can be generated, and the Moire pattern can appear. In short, when the pixel points of the camera sensor and the pixel points of the display are close and coincide, the human eyes are sensitive to the coincidence, so that the moire fringes are seen.

Since moire is a stripe of alternating bright and dark, if moire is displayed, a pattern of peaks and valleys inevitably appears on the luminance curve. Thus, moire variation information may include: the presence or absence of moire, the brightness curve of moire, the moire pattern, etc. are not particularly limited.

Therefore, if the first image and the second image are the copied images, the fused image will have significant moire features. Therefore, by extracting the moire variation information from the fused image, it is possible to determine whether the first image and the second image are the copied images in reverse.

In this embodiment of the present description, determining whether the first image and the second image are the copied images by using moire variation information in the fused image may include:

comparing the moire variation information to a moire variation threshold;

and determining whether the first image and the second image are the copied images according to the comparison result.

Moire pattern variation information may be derived from historical image samples. Specifically, the moire analysis statistics may be performed on the historical image sample. It is also possible to use historical image samples for model training to configure moir é change thresholds and other parameters and logic in the model.

In another embodiment, the determination of the copied image may be based on whether moire variation information is extracted.

By using the method described in the embodiment of the present specification, since the first image and the second image have different acquisition conditions and have different moire patterns, moire pattern change information represented in the fused image is enhanced, so that moire pattern features in the fused image are more obvious. This can promote the recognition accuracy to the moire pattern characteristic, further promotes the recognition accuracy to the image of shooing.

Fig. 2 is a flowchart of a model training method according to an embodiment of the present disclosure. The model trained by the method can be used for a reproduction image recognition scheme.

Step 202: acquiring a fused image sample, wherein the fused image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions.

In the embodiment of the present specification, the first sample image and the second sample image of the specified object may be two-frame sample images of the specified object. The fused image sample can comprise a large number of positive and negative double-frame image samples, the positive double-frame sample refers to a double-frame real image under different acquisition conditions, and the negative double-frame image sample refers to a double-frame reproduced image under the condition of unavailable acquisition.

The obtaining of the fused image sample may be extracting a first sample image and a second sample image from a database, and performing image fusion on the first sample image and the second sample image based on the identified specified object. Wherein the image fusion scheme can refer to the above content shown in step 103, and is not detailed here.

Step 204: and training a model by using the fused image sample so as to identify the moire variation information from the fused image by using the model, and judging whether the first image and the second image forming the fused image are the copied image or not by using the moire variation information.

Thus, the training model comprises a function of recognizing the moire variation information by the training model and a training moire variation threshold value, so that the model can compare the moire variation threshold value with the moire variation information to be used as a basis for judging whether the first image and the second image are copied.

The model described in the embodiments of the present specification may be a Neural Network model, such as a Convolutional Neural Network model CNN (generic Neural Network), and other types of Neural Network models and other types of models may also be selected as needed.

Fig. 3 is a flowchart of a method for recognizing a copied image according to an embodiment of the present disclosure. The method specifically utilizes the model shown in FIG. 2 to identify the copied image.

Step 301 may refer to the content of step 101 above, and step 303 may refer to the content of step 103 above, and is not limited in particular.

Step 303: identifying moire variation information from a fusion image by using a model, and judging whether the first image and the second image are copied images or not by using the moire variation information, wherein the model is obtained by training a fusion image sample, the fusion image sample is obtained by carrying out image fusion on a first sample image and a second sample image of a specified object based on the specified object, and the first sample image and the second sample image are acquired under different acquisition conditions.

In this embodiment, the model may output a comparison result of moire variation information and a moire variation threshold, and then may further determine whether the first image and the second image are the copied image according to the comparison result obtained from the model. For example, if the moire variation information exceeds a moire variation threshold, the first image and the second image are judged to be the copied image; on the contrary, if the moire variation information does not exceed the moire variation threshold, it is determined that the first image and the second image are not the reproduced image.

The model may output a probability value that the first image and the second image are the copied images, and then whether the first image and the second image are the copied images may be directly determined according to the probability value.

Fig. 4 is a flowchart of a method for recognizing a copied photo according to an embodiment of the present disclosure. The execution subject of the method may be a client or a server corresponding to the client, and is not limited in particular here.

Step 402: the target document is identified in a first and a second document photo, respectively, the first and second images being captured under different capture conditions. The target certificate described in the embodiment of the present specification may be an example of the target object in the embodiment shown in fig. 1.

Step 404: and carrying out image fusion on the first certificate photo and the second certificate photo based on the identified target certificate.

Step 406: and judging whether the first certificate photo and the second certificate photo are the reproduction certificate photo or not by using the Moire pattern change information in the fused image.

Fig. 5 is a flowchart of an application example of a method for recognizing a copied photo according to an embodiment of the present disclosure.

Step 501: the method comprises the steps of collecting a first certificate photo and a second certificate photo of two different shooting angles, wherein the photos both comprise certificate outlines.

Step 503: and projecting the second certificate photo onto the first certificate photo to ensure that the certificate outline in the second certificate photo is consistent with the certificate outline in the first certificate photo. The method specifically comprises the following steps:

firstly, carrying out angular point detection on the first certificate photo, and storing the positions of four detected angular points as reference points;

and carrying out angular point detection on the second certificate photo, and projecting the obtained angular points to the four angular points of the first certificate photo through affine change to obtain a projection matrix M. And projecting the second certificate photo onto the first certificate photo through the projection matrix M to obtain the projected certificate photo. Wherein, the certificate outline contained in the projection certificate photo is completely consistent with the certificate outline in the first certificate photo.

Step 505: and fusing the first certificate photo and the projection certificate photo, wherein the fused image is characterized by data, and if the image is copied, obvious moire change can be observed.

Step 507: and sending the data to a pre-trained CNN model for confidence score output, and then judging whether the first certificate photo and the second certificate photo are the copied certificate photo according to the output confidence score.

At this time, the dimension of the data may be 1x6xWxH, where 1 denotes that the blocksize is 1, 6 denotes that the blocksize is 6 channels, W denotes the picture width of the first certificate photo and the projection certificate photo, and H denotes the length of the picture of the first certificate photo and the projection certificate photo.

Fig. 6 is a structural diagram of a copied image recognition apparatus according to an embodiment of the present disclosure.

The apparatus may include:

an identification module 601, configured to identify a target object in a first image and a second image, respectively, where the first image and the second image are acquired under different acquisition conditions;

an image fusion module 602, configured to perform image fusion on the first image and the second image based on the identified target object;

the determining module 603 determines whether the first image and the second image are the copied images by using the moire variation information in the fused image.

Optionally, performing image fusion on the first image and the second image based on the identified target object, including:

By using the scheme provided by the embodiment of the specification, the moire variation information represented in the fused image is enhanced because the moire presented in the first image and the moire presented in the second image have different acquisition conditions, so that the moire feature in the fused image is more obvious. This can promote the recognition accuracy to the moire pattern characteristic, further promotes the recognition accuracy to the image of shooing.

Based on the same inventive concept, an embodiment of the present specification further provides an electronic device, including:

a processor; and

Based on the same inventive concept, there is also provided in the embodiments of this specification a computer-readable storage medium comprising a computer program for use with an electronic device, the computer program being executable by a processor to perform the steps of:

Fig. 7 is a structural diagram of a device for recognizing a copied photo according to an embodiment of the present disclosure. The apparatus may include:

an identification module 701, configured to identify a target certificate in a first certificate photo and a second certificate photo respectively, where the first image and the second image are acquired under different acquisition conditions;

an image fusion module 702, for performing image fusion on the first certificate photo and the second certificate photo based on the identified target certificate;

the judging module 703 judges whether the first certificate photo and the second certificate photo are the copied certificate photo by using the moire variation information in the fused image.

a processor; and

Fig. 8 is a block diagram of a model training apparatus according to an embodiment of the present disclosure. The device comprises:

an obtaining module 801, configured to obtain a fused image sample, where the fused image sample is obtained by performing image fusion on a first sample image and a second sample image of a specified object based on the specified object, where the first sample image and the second sample image are acquired under different acquisition conditions;

the training module 802 trains a model using the fused image sample, so as to identify the moire variation information from the fused image using the model, and determine whether the first image and the second image forming the fused image are the copied image using the moire variation information.

a processor; and

Fig. 9 is a structural diagram of a copied image recognition apparatus according to an embodiment of the present disclosure. The apparatus may include:

an identifying module 901, configured to identify a target object in a first image and a second image, respectively, where the first image and the second image are acquired under different acquisition conditions;

an image fusion module 902, which performs image fusion on the first image and the second image based on the identified target object;

the determining module 903 is configured to recognize moire variation information from a fusion image by using a model, and determine whether the first image and the second image are copied images by using the moire variation information, where the model is obtained by training a fusion image sample, and the fusion image sample is obtained by performing image fusion on a first sample image and a second sample image of a specified object based on the specified object, where the first sample image and the second sample image are acquired under different acquisition conditions.

a processor; and

Fig. 10 is a more specific hardware structure diagram of a computing device provided in an embodiment of the present specification, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.

The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.

The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.

The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.

The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).

Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.

It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for recognizing a copied image comprises the following steps:

2. The method of claim 1, further comprising, prior to identifying the target object in the first image and the second image, respectively:

and acquiring the first image and the second image based on different acquisition conditions in sequence according to an image acquisition instruction aiming at the target object.

3. The method of claim 1, further comprising, prior to identifying the target object in the first image and the second image, respectively:

sending image acquisition prompt information aiming at the target object to prompt a user to acquire images of the target object based on different acquisition conditions;

receiving the uploaded first image and second image.

4. The method of claim 1, image fusing the first and second images based on the identified target object, comprising:

5. The method of claim 4, wherein registering the outer contour of the target object identified in the second image with the target object identified in the first image comprises:

projecting the second image to the first image by projecting a reference point in the second image to a reference point in the first image to obtain a second image projection image;

performing image fusion on the first image and the second image based on the registered target object, including:

and carrying out image fusion on the first image and the second image projection image.

6. The method of claim 1, wherein determining whether the first image and the second image are the copied image by using the moire variation information in the fused image comprises:

comparing the moire variation information to a moire variation threshold;

7. A method for identifying a copied photo comprises the following steps:

8. A model training method, comprising:

9. A method for recognizing a copied image comprises the following steps:

10. A reproduction image recognition apparatus comprising:

11. The apparatus of claim 10, the image fusing the first and second images based on the identified target object, comprising:

12. A reproduction photo identification device comprising:

13. A model training apparatus comprising:

14. A reproduction image recognition apparatus comprising:

15. An electronic device, comprising:

a processor; and

16. An electronic device, comprising:

a processor; and

17. An electronic device, comprising:

a processor; and

18. An electronic device, comprising:

a processor; and