Method and apparatus for authenticating legality of camera

Abstract

The disclosure provides a legality authentication method and device of a camera. The method comprises collecting initial image information transmitted after the camera is connected to a server; performing dimension reduction sampling processing on the initial image information to obtain target image information; scanning the target image information using a plurality of two-dimensional windows and performing decomposition and superposition processing on the information in each two-dimensional window to obtain a coefficient matrix set; obtaining a target matrix according to a peak value region in each coefficient matrix in the coefficient matrix set, wherein the peak value region is determined according to a preset position parameter; processing the target matrix using a maximum likelihood algorithm to obtain hardware fingerprint information of the camera; and determining the legality of the camera according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is the hardware fingerprint of a legal device corresponding to the device attribute information of the camera.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of camera devices, and more particularly, to a legality authentication method and device of a camera device, an electronic device, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] In many unattended scenes of buildings or fields, cameras are generally allowed to transmit pictures normally without additional judgment on whether the device itself is safe. An attacker can privately replace the camera with a cheaper camera device. More importantly, the illegal device replaced by the attacker may be specially modified to transmit data to a third-party server through an illegal channel while transmitting normal monitoring pictures.

[0003] The commonly used method to determine the legality of the device is to encode the device, and then perform security authentication based on the encoding, such as judging whether the device is originally installed by the factory serial number or MAC address of the device. However, these encodings have the possibility of being tampered with and forged. The above-mentioned legality authentication method cannot accurately identify the tampered device, thereby greatly affecting the security of data. SUMMARY

[0004] Therefore, the present disclosure provides a legality authentication method and device of a camera device, an electronic device, a computer readable storage medium, and a computer program product.

[0005] One aspect of an embodiment of the present disclosure provides a legality authentication method of a camera device, comprising:

[0006] Collecting initial image information transmitted after the camera device is connected to a server;

[0007] Performing dimension reduction sampling processing on the initial image information to obtain target image information;

[0008] Scanning the target image information using a plurality of two-dimensional windows, and performing decomposition and superposition processing in the information in each two-dimensional window to obtain a coefficient matrix set, wherein the coefficient matrix set includes an approximate coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal detail coefficient matrix;

[0009] Obtaining a target matrix according to a peak value region in each coefficient matrix in the coefficient matrix set, wherein the peak value region is determined according to a preset position parameter;

[0010] Processing the target matrix using a maximum likelihood algorithm to obtain hardware fingerprint information of the camera device;

[0011] According to the hardware fingerprint information and the reference fingerprint information, the legality of the camera is determined, wherein the reference fingerprint information is the hardware fingerprint of the legal device corresponding to the device attribute information of the camera.

[0012] According to the embodiment of the present disclosure, before the image information is acquired, the method further comprises:

[0013] A data interface is called to acquire a plurality of device attribute information of the camera, wherein the device attribute information represents the unique identifier of the camera;

[0014] Each device attribute information is format-converted and spliced to obtain the encoding information of the camera;

[0015] The encoding information is operated based on a hash function to obtain target identifier information;

[0016] In the case that the same reference identifier information as the target identifier information exists in the database, the image information captured by the camera is acquired, wherein the reference identifier information is the identifier information of the legal device identical to the device attribute information.

[0017] According to the embodiment of the present disclosure, the target image information is scanned by using a plurality of two-dimensional windows, and decomposition and superposition processing is performed on the information in each two-dimensional window to obtain a coefficient matrix set, comprising:

[0018] The target image information is decomposed by using a plurality of two-dimensional windows to obtain a plurality of wavelet function data, wherein the two-dimensional window has the functions of stretching and shifting;

[0019] For each two-dimensional window, a plurality of wavelet function data corresponding to the two-dimensional window are superimposed to obtain a coefficient set, wherein the coefficient set includes an approximation coefficient, a horizontal detail coefficient, a vertical detail coefficient, and a diagonal detail coefficient;

[0020] For any coefficient in the coefficient set, the coefficient matrix corresponding to the coefficient is obtained according to a plurality of coefficients of a plurality of two-dimensional windows;

[0021] The coefficient matrix set is constructed according to a plurality of coefficient matrices.

[0022] According to the embodiment of the present disclosure, the target matrix is obtained according to the peak value region in each coefficient matrix in the coefficient matrix set, comprising:

[0023] For each coefficient matrix, the coefficient matrix is detected to determine the main peak value and a plurality of secondary peak values of the coefficient matrix;

[0024] determine a peak value region of the main peak value and the secondary peak value based on the preset position parameter;

[0025] determine the target matrix from the coefficient matrix based on the peak value region.

[0026] According to an embodiment of the present disclosure, the determining the target matrix from the coefficient matrix based on the peak value region comprises:

[0027] For each of the coefficient matrix, determine a region matrix data from the coefficient matrix based on the peak value region;

[0028] perform a phase stretch transform on the region matrix data to obtain an edge sharpness value;

[0029] construct a transition matrix according to the edge sharpness value and the region matrix data;

[0030] generate the target matrix according to a plurality of the transition matrix.

[0031] According to an embodiment of the present disclosure, the processing a plurality of the target matrix by using a maximum likelihood algorithm to obtain the hardware fingerprint information of the camera device comprises

[0032] In a case where the number of the initial image information is one or more, generate the hardware fingerprint information according to data of the target matrix corresponding to each of the initial image information and noise residuals of the target matrix;

[0033] wherein the hardware fingerprint information K is shown in the following formula:

[0034]

[0035] wherein N represents the number of the initial image information, W i represents data of the target matrix, I i represents noise residuals of the response non-uniformity extracted from the target matrix, and i represents the i-th initial image information.

[0036] According to an embodiment of the present disclosure, the determining the legality of the camera device according to the hardware fingerprint information and the reference fingerprint information comprises:

[0037] In a case where the number of the initial image information is one or more, perform linear dimension reduction processing on each of the hardware fingerprint information by using a principal component analysis method to obtain one or more target fingerprint information;

[0038] calculate a similarity between each of the target fingerprint information and the reference fingerprint information by using a chaotic polynomial expansion method;

[0039] determine a similarity average value according to the plurality of similarity values;

[0040] In a case where the similarity average value meets a preset threshold value, determine the camera device as a legal camera device;

[0041] In a case where the similarity average value does not meet the preset threshold value, determine the camera device as an illegal camera device.

[0042] According to an embodiment of the present disclosure, the dimensionality reduction sampling processing on the initial image information to obtain the target image information comprises:

[0043] eliminating irrelevant noise in the initial image information to obtain first transition image information, wherein the irrelevant noise is generated by a color filter matrix;

[0044] performing smoothing processing on the first transition image information based on a minimum mean square error algorithm to obtain second transition image information;

[0045] performing initial dimensionality reduction processing on the second transition image information based on a preset sampling rule to obtain the target image information.

[0046] According to an embodiment of the present disclosure, the initial dimensionality reduction processing on the second transition image information based on a preset sampling rule to obtain the target image information comprises:

[0047] scanning the second transition image using a preset sampling rule to obtain a plurality of window data, wherein the preset sampling rule comprises scanning using an m×k sampling window with a horizontal step length m and a vertical step length k;

[0048] for each window data, processing the window data using a maximum sampling function to obtain a plurality of dimensionality reduction image information;

[0049] generating the target image information according to the plurality of dimensionality reduction image information.

[0050] Another aspect of an embodiment of the present disclosure provides a legality authentication device of a camera device, comprising:

[0051] a collection module configured to collect initial image information transmitted by the camera device after connecting to a server;

[0052] a dimensionality reduction sampling module configured to perform dimensionality reduction sampling processing on the initial image information to obtain target image information;

[0053] a scanning and superimposing module, configured to scan the target image information by using a plurality of two-dimensional windows, and perform decomposition and superimposition processing on information in each of the two-dimensional windows to obtain a coefficient matrix set, wherein the coefficient matrix set includes an approximation coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal detail coefficient matrix;

[0054] a first determining module, configured to obtain a target matrix according to a peak region in each coefficient matrix in the coefficient matrix set, wherein the peak region is determined according to a preset position parameter;

[0055] a processing module, configured to process the target matrix by using a maximum likelihood algorithm to obtain the hardware fingerprint information of the camera device;

[0056] a second determining module, configured to determine the legality of the camera device according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is the hardware fingerprint of a legal device corresponding to the device attribute information of the camera device.

[0057] Another aspect of the embodiments of the present disclosure provides an electronic device, including: one or more processors; a memory configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0058] Another aspect of the embodiments of the present disclosure provides a computer-readable storage medium, which stores computer-executable instructions, the instructions being used to implement the method as described above when executed.

[0059] Another aspect of the embodiments of the present disclosure provides a computer program product, which includes computer-executable instructions, the instructions being used to implement the method as described above when executed.

[0060] According to the embodiments of the present disclosure, by performing scanning and decomposition and superimposition processing on the target image information obtained by performing dimension reduction sampling processing on the initial image information transmitted by the camera device, a coefficient matrix set corresponding to the initial image information can be obtained, a target matrix can be obtained through a peak region of each coefficient matrix in the coefficient matrix set, and the hardware fingerprint information of the camera device can be determined according to the target matrix. The legality of the camera device can be determined by comparing the hardware fingerprint information with reference fingerprint information of a corresponding legal device, thereby avoiding the situation that an illegal camera device with tampered device attribute information replaces a legal camera device in some places, and improving the security of image information and the like. BRIEF DESCRIPTION OF DRAWINGS

[0061] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which:

[0062] Figure 1 An exemplary system architecture to which the legality authentication method according to embodiments of the present disclosure can be applied is schematically illustrated;

[0063] Figure 2 A flowchart of the legality authentication method according to embodiments of the present disclosure is schematically illustrated;

[0064] Figure 3 A schematic diagram of initial image information according to embodiments of the present disclosure is schematically illustrated;

[0065] Figure 4 A generation schematic diagram of target fingerprint information according to embodiments of the present disclosure is schematically illustrated;

[0066] Figure 5 A visualization diagram of target fingerprint information according to embodiments of the present disclosure is schematically illustrated;

[0067] Figure 6 A schematic diagram of initial image information according to another embodiment of the present disclosure is schematically illustrated;

[0068] Figure 7 A flowchart of the similarity calculation according to embodiments of the present disclosure is schematically illustrated;

[0069] Figure 8 A block diagram of a legality authentication apparatus according to embodiments of the present disclosure is schematically illustrated;

[0070] Figure 9 A block diagram of an electronic device suitable for implementing the above-described method according to embodiments of the present disclosure is schematically illustrated. DETAILED DESCRIPTION

[0071] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It is to be understood, however, that the description is merely exemplary of the present disclosure, and is not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it would be apparent to one skilled in the art that the present disclosure can be practiced without these specific details. In other instances, well-known structures and functions have not been described in detail in order to avoid obscuring aspects of the present disclosure.

[0072] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. As used herein, the term "includes" and "including" means the inclusion without limitation of the stated features, steps, operations, and / or components.

[0073] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the use of terms herein are intended to be construed in a manner consistent with the context of the present description, and should not be construed in an idealized or overly formal way.

[0074] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should be generally construed that the meaning is understood by one of ordinary skill in the art as it is commonly used (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, a system having B alone, a system having C alone, a system having both A and B, a system having both A and C, a system having both B and C, and / or a system having A, B, and C, etc.). In the case of using expressions similar to "at least one of A, B, or C, etc.", it should be generally construed that the meaning is understood by one of ordinary skill in the art as it is commonly used (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, a system having B alone, a system having C alone, a system having both A and B, a system having both A and C, a system having both B and C, and / or a system having A, B, and C, etc.).

[0075] Embodiments of the present disclosure provide a legality authentication method and legality authentication device of a camera device. The method comprises collecting initial image information transmitted after the camera device is connected to a server; performing dimension reduction sampling processing on the initial image information to obtain target image information; scanning the target image information using a plurality of two-dimensional windows and performing decomposition and superposition processing on information in each two-dimensional window to obtain a coefficient matrix set, wherein the coefficient matrix set comprises an approximate coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal line detail coefficient matrix; obtaining a target matrix according to a peak value region in each coefficient matrix in the coefficient matrix set, wherein the peak value region is determined according to a preset position parameter; processing the target matrix using a maximum likelihood algorithm to obtain hardware fingerprint information of the camera device; and determining the legality of the camera device according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is the hardware fingerprint of a legal device corresponding to device attribute information of the camera device.

[0076] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure, and application of the information of the place and the user, etc. comply with relevant laws and regulations, necessary security measures are taken, and the public order and good customs are not violated.

[0077] In the technical solutions of the present disclosure, the authorization or consent of the place unit or the user is obtained before the image of the place or the user personal information is acquired or collected.

[0078] Figure 1An exemplary system architecture 100 to which the legality authentication method according to embodiments of the present disclosure can be applied is shown schematically. It should be noted that Figure 1 The system architecture shown is merely an example of the system architecture to which embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but does not mean that embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0079] As Figure 1 The system architecture 100 according to this embodiment can include terminal devices 101, 102, 103, a network 104, a server 105 and a camera 106, as shown. The network 104 is a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired and / or wireless communication links, etc.

[0080] A user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 101, 102, 103, such as legality authentication applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0081] The terminal devices 101, 102, 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, laptop computers and desktop computers, etc.

[0082] The server 105 can be a server that provides various services, such as a background management server that provides support for websites browsed by users using the terminal devices 101, 102, 103 (only as an example). The background management server can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information or data obtained or generated according to user requests, etc.) to the terminal devices.

[0083] The camera 106 can be a camera or the like with image capturing function, which can capture images within a target place for monitoring or data collection of the target place, and the images can also be used to authenticate the legality of the camera 106.

[0084] It should be noted that the legality authentication method provided by the embodiments of the present disclosure can be generally executed by the server 105. Correspondingly, the legality authentication apparatus provided by the embodiments of the present disclosure can be generally arranged in the server 105. The legality authentication method provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal device 101, 102, 103 and / or the server 105. Correspondingly, the legality authentication apparatus provided by the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 105 and capable of communicating with the terminal device 101, 102, 103 and / or the server 105. Alternatively, the legality authentication method provided by the embodiments of the present disclosure can also be executed by the terminal device 101, 102, or 103, or by other terminal devices different from the terminal device 101, 102, or 103. Correspondingly, the legality authentication apparatus provided by the embodiments of the present disclosure can also be arranged in the terminal device 101, 102, or 103, or in other terminal devices different from the terminal device 101, 102, or 103.

[0085] It should be understood that Figure 1 The number of terminal devices, networks, servers and cameras in the above description is only illustrative. According to the needs of implementation, there can be any number of terminal devices, networks, servers and cameras.

[0086] Figure 2 A flowchart of a legality authentication method according to an embodiment of the present disclosure is schematically shown. Figure 3 A schematic diagram of initial image information according to an embodiment of the present disclosure is schematically shown.

[0087] As Figure 2 The legality authentication method of the camera device includes operations S201-S206.

[0088] In operation S201, initial image information transmitted after the camera device is connected to the server is collected.

[0089] In operation S202, the initial image information is processed by dimension reduction sampling to obtain target image information.

[0090] In operation S203, the target image information is scanned by using a plurality of two-dimensional windows, and decomposition and superposition processing is performed in the information in each two-dimensional window to obtain a coefficient matrix set, wherein the coefficient matrix set includes an approximate coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix and a diagonal detail coefficient matrix.

[0091] In operation S204, according to a peak value region in each coefficient matrix in the coefficient matrix set, a target matrix is obtained, wherein the peak value region is determined according to a preset position parameter.

[0092] In operation S205, the target matrix is processed by using a maximum likelihood algorithm to obtain the hardware fingerprint information of the camera device.

[0093] In operation S206, the legality of the camera device is determined according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is the hardware fingerprint of a legal device corresponding to the device attribute information of the camera device.

[0094] According to an embodiment of the present disclosure, the hardware fingerprint information is a unique feature embodied in a signal based on the hardware features of the device. The feature of the fingerprint identification changes over time during the video shooting process of the camera device, but the change is small within a certain period of time, for example, within a month, the generated hardware fingerprint information may have a difference of 5% to 10%.

[0095] According to an embodiment of the present disclosure, the camera device can be an image acquisition device for transmitting video or pictures, such as a camera, a video monitoring device, etc. The preset position parameter can refer to the size of the area around the point where the peak value and / or the secondary peak value are reserved, for example, in the case of a preset position parameter t = 50, it represents the points within the range of 50 above, below, left and right of the point where the peak value and / or the secondary peak value are reserved.

[0096] According to an embodiment of the present disclosure, after the camera device establishes a connection with the server and performs normal information transmission, at this time, any number of image frames transmitted by the camera device can be acquired as initial image information, as shown in FIG. 2B, the initial image information is processed by dimension reduction sampling to make only a part of the dimensions in the initial image information be reserved in the obtained target image information, for example, only 1 / m x k dimensions are reserved, wherein m x k is a sampling window parameter used in the dimension reduction sampling processing. Figure 3

[0097] According to an embodiment of the present disclosure, the target image information is scanned by using n two-dimensional windows, and decomposition and superposition processing is performed in the information in each two-dimensional window to obtain a set of coefficient matrices. The target matrix is obtained according to the peak value area in each coefficient matrix in the set of coefficient matrices. The hardware fingerprint information of the camera device is obtained by processing the target matrix by using a maximum likelihood algorithm. According to the similarity between the hardware fingerprint information and the reference fingerprint information of a legal device corresponding to the device attribute information of the camera device, the legality of the camera device can be determined, that is, whether the camera device is an original legal device or has been replaced by an illegal person.

[0098] ​According to the embodiment of the present disclosure, by scanning and decomposing and superimposing the target image information obtained by the dimensionality reduction sampling processing on the initial image information transmitted by the camera, a coefficient matrix set corresponding to the initial image information can be obtained, and a target matrix can be obtained through the peak region of each coefficient matrix in the coefficient matrix set. According to the target matrix, the hardware fingerprint information of the camera can be determined, and the legality of the camera can be determined by comparing the hardware fingerprint information with the reference fingerprint information of the corresponding legal device, thereby avoiding the situation that the illegal camera device that tampers with the device attribute information replaces the legal camera device in some places, and improving the security of image information and the like.

[0099] According to the embodiment of the present disclosure, before the image information is obtained, the following operation is further included:

[0100] The data interface is called to obtain a plurality of device attribute information of the camera, wherein the device attribute information represents the unique identifier of the camera. Each device attribute information is subjected to format conversion and splicing processing to obtain the encoding information of the camera. The encoding information is subjected to operation processing based on a hash function to obtain target identification information. In the case that the same reference identification information as the target identification information exists in the database, the image information photographed by the camera is obtained, wherein the reference identification information is the identification information of the legal device with the same device attribute information.

[0101] According to the embodiment of the present disclosure, before the comparison of the hardware fingerprint information is performed, the device attribute information of the camera itself can be compared first. If the comparison is inconsistent, it is determined that the camera is an illegal camera device without the comparison of the hardware fingerprint information. If the comparison is consistent, the comparison of the hardware fingerprint information is performed to further determine the legality of the camera.

[0102] According to the embodiment of the present disclosure, first, the serial number, MAC address and the like of the camera shown in Table 1 are obtained by calling the API interface, each device attribute information is converted into binary code and spliced to obtain the encoding information, and then the hash function is used to calculate the encoding information to obtain the target identification information. The reference identification information of the legal device stored in the database is searched to determine whether there is the same reference identification information as the target identification information. If there is, it is preliminarily determined that the camera is a legal camera device, and then the comparison of the hardware fingerprint information is performed to further determine the legality of the camera. If there is not, it is determined that the camera is an illegal camera device.

[0103] Table 1

[0104]

[0105] According to the embodiment of the present disclosure, the device attribute information of the camera is operated and preliminarily judged whether it is a legal camera before the comparison of the hardware fingerprint information is performed to determine the legality of the camera, so that the system occupancy rate of the legality authentication device of the camera can be reduced, and the rate of the legality authentication can be improved.

[0106] According to the embodiment of the present disclosure, the target image information is scanned by using a plurality of two-dimensional windows, and decomposition and superposition processing is performed on the information in each two-dimensional window to obtain a coefficient matrix set, including the following operations:

[0107] The target image information is decomposed by using a plurality of two-dimensional windows to obtain a plurality of wavelet function data, wherein the two-dimensional window has the functions of stretching and shifting. For each two-dimensional window, the plurality of wavelet function data corresponding to the two-dimensional window are superimposed to obtain a coefficient set, wherein the coefficient set includes an approximation coefficient, a horizontal detail coefficient, a vertical detail coefficient, and a diagonal detail coefficient. For any coefficient in the coefficient set, a coefficient matrix corresponding to the coefficient is obtained according to a plurality of coefficients of the plurality of two-dimensional windows. The coefficient matrix set is constructed according to the plurality of coefficient matrices.

[0108] According to the embodiment of the present disclosure, n two-dimensional windows are used to scan data. In each window, the target image information is gradually multi-scaled and refined by stretching and shifting operations, and the target image information is decomposed into a series of superpositions of wavelet functions. The approximation coefficient, the horizontal detail coefficient, the vertical detail coefficient, and the diagonal detail coefficient of the wavelet change after the output of each two-dimensional window are retained. Therefore, the corresponding coefficient matrix is constructed according to the same type of coefficients output by the plurality of two-dimensional windows, and finally the coefficient matrix set is constructed according to the plurality of coefficient matrices.

[0109] According to the embodiment of the present disclosure, the target matrix is obtained according to the peak value region in each coefficient matrix in the coefficient matrix set, including the following operations:

[0110] For each coefficient matrix, the coefficient matrix is detected to determine the main peak value and a plurality of secondary peak values of the coefficient matrix. Based on a preset position parameter, the peak value regions of the main peak value and the secondary peak values are determined respectively. Based on the peak value regions, the target matrix is determined from the coefficient matrix.

[0111] According to the embodiment of the present disclosure, for the main peak value and the n secondary peak values detected by each coefficient matrix, the peak value regions of the main peak value and the secondary peak values are determined according to a preset position parameter t. Based on the peak value regions, the target matrix is determined from the coefficient matrix by retaining the information within the neighborhood t of the main peak value and the secondary peak values.

[0112] According to the embodiment of the present disclosure, the target matrix is determined from the coefficient matrix based on the peak value region, including the following operations:

[0113] For each coefficient matrix, based on the peak region, region matrix data is determined from the coefficient matrix; the region matrix data is subjected to phase stretching transformation processing to obtain an edge sharp value; according to the edge sharp value and the region matrix data, a transition matrix is constructed; and according to the plurality of transition matrices, a target matrix is generated.

[0114] According to an embodiment of the present disclosure, the region matrix data is information in the above-mentioned range t. The region matrix data is subjected to phase stretching transformation processing to obtain an edge sharp value. In the phase stretching transformation, a sharp transition can be found according to the phase kernel of the phase stretching transformation, and a fixed value (for example, 0) is returned for other phases, so that a transition matrix can be constructed according to the edge sharp value and the region matrix data. According to the plurality of transition matrices of the plurality of coefficient matrices, the target matrix can be generated. The hardware fingerprint information of the camera device can be obtained by processing the target matrix using the maximum likelihood algorithm.

[0115] According to an embodiment of the present disclosure, the hardware fingerprint information of the camera device is obtained by processing the plurality of target matrices using the maximum likelihood algorithm, including the following operations:

[0116] In the case where the number of initial image information is one or more, the hardware fingerprint information is generated according to the data of the target matrix corresponding to each initial image information and the noise residual error of the target matrix.

[0117] Wherein, the hardware fingerprint information K is shown in formula (1):

[0118]

[0119] Wherein, N represents the number of initial image information, W i represents the data of the target matrix, I i represents the response non-uniformity (PRNU) noise residual error extracted from the target matrix, and i represents the i th initial image information.

[0120] Figure 4 The generation of the target fingerprint information according to an embodiment of the present disclosure is schematically shown. Figure 5 The visualization of the target fingerprint information according to an embodiment of the present disclosure is schematically shown.

[0121] According to an embodiment of the present disclosure, the legality of the camera device is determined according to the hardware fingerprint information and the reference fingerprint information, including the following operations:

[0122] When there are one or more initial image information, Principal Component Analysis (PCA) is used to perform linear dimensionality reduction on each hardware fingerprint information to obtain one or more target fingerprint information. The similarity between each target fingerprint information and the reference fingerprint information is calculated using Polynomial Chaos Expansions (PCE). The average similarity is determined based on multiple similarities. If the average similarity meets a preset threshold, the camera device is determined to be a legitimate camera device. If the average similarity does not meet the preset threshold, the camera device is determined to be an illegitimate camera device.

[0123] According to one embodiment of this disclosure, when the initial image information consists of only one element, principal component analysis is used to perform linear dimensionality reduction on the hardware fingerprint information, such as... Figure 4 As shown, the hardware fingerprint information is transformed into an orthogonal space, allowing its main information to be stored within a smaller dimensional range, thus obtaining a target fingerprint information K′. The visualization image of the target fingerprint information K′ is shown below. Figure 5 As shown, the similarity between the target fingerprint information K′ and the reference fingerprint information is calculated using the chaotic polynomial expansion method. The legitimacy of the camera device is determined by judging whether the similarity meets a preset threshold. The preset threshold is set according to the specific circumstances, for example, it can be 0.8.

[0124] According to another embodiment of this disclosure, when there are multiple initial image information, the hardware fingerprint information is linearly reduced using principal component analysis to obtain multiple target fingerprint information; the similarity between each target fingerprint information K′ and the reference fingerprint information is calculated using chaotic polynomial expansion, and the average similarity of multiple similarities is obtained. The legitimacy of the camera device can be determined based on whether the average similarity meets a preset threshold.

[0125] According to embodiments of this disclosure, dimensionality reduction sampling processing is performed on initial image information to obtain target image information, including the following operations:

[0126] Irrelevant noise in the initial image information is eliminated to obtain the first transition image information, wherein the irrelevant noise is generated by the color filtering matrix; the first transition image information is smoothed based on the minimum mean square error algorithm to obtain the second transition image information; the second transition image information is initially reduced based on the preset sampling rules to obtain the target image information.

[0127] According to an embodiment of the present disclosure, irrelevant noise error caused by a color filter array (CFA) is eliminated by subtracting the mean value of the row and column where each pixel value in the initial image information from the initial image information to obtain first transition image information, and then the first transition image information is smoothed based on a minimum mean square error filter to remove abnormal outlier noise data to obtain second transition image information. The second transition image information is initially reduced in dimension based on a preset sampling rule to obtain target image information.

[0128] According to an embodiment of the present disclosure, the second transition image information is initially reduced in dimension based on a preset sampling rule to obtain target image information, including the following operations:

[0129] The second transition image is scanned using a preset sampling rule to obtain a plurality of window data, wherein the preset sampling rule includes scanning using an m×k sampling window with a horizontal step size m and a vertical step size k. For each window data, the window data is processed using a maximum sampling function to obtain a plurality of reduced dimension image information. The target image information is generated based on the plurality of reduced dimension image information.

[0130] According to an embodiment of the present disclosure, the second transition image is scanned using an m×k sampling window with a horizontal step size m and a vertical step size k to obtain a plurality of window data. In each sampling window, the window data is processed using a maximum sampling function algorithm to retain reduced dimension image information of 1 / m×k of the original image dimension. The target image information is generated based on the plurality of reduced dimension image information.

[0131] Figure 6 An illustrative diagram of initial image information according to another embodiment of the present disclosure is schematically shown. Figure 7 An illustrative diagram of target fingerprint information according to an embodiment of the present disclosure is schematically shown.

[0132] In an illustrative embodiment, when the legality of 58 cameras installed in a certain research institute is verified, initial image information of the corresponding cameras is obtained, and part of the initial image information transmitted by the cameras is as shown in Figure 6

[0133] According to an embodiment of the present disclosure, referring to Figure 7 ​The initial image information is subjected to dimension reduction sampling processing, for example, color space conversion is performed on the initial image information to eliminate irrelevant noise in the initial image information, and finally target image information is obtained. Based on the target image information, a target matrix can be obtained, noise residuals w of the target matrix are extracted, and the hardware fingerprint information K of the camera can be obtained according to the data of the target matrix processed by the maximum likelihood algorithm and the noise residuals of the target matrix. The hardware fingerprint information K is subjected to linear dimension reduction processing by using principal component analysis (PCA) to obtain target fingerprint information K'. The similarity between each target fingerprint information and reference fingerprint information is calculated by using a chaotic polynomial expansion method (PCE), that is, the similarity is determined by calculating the peak value capability correlation ratio between the two in the figure. Whether the similarity meets a preset threshold value can be used to determine the legitimacy of the camera device.

[0134] Figure 8 A block diagram of a legitimacy authentication device according to an embodiment of the present disclosure is schematically shown.

[0135] As shown in Figure 8 The legitimacy authentication device 800 of the camera device includes a collection module 810, a down-sampling module 820, a scanning and superimposition module 830, a first determination module 840, a processing module 850, and a second determination module 860.

[0136] The collection module 810 is configured to collect initial image information transmitted by the camera device after being connected to a server.

[0137] The down-sampling module 820 is configured to perform dimension reduction sampling processing on the initial image information to obtain target image information.

[0138] The scanning and superimposition module 830 is configured to scan the target image information by using a plurality of two-dimensional windows, and perform decomposition and superimposition processing on information in each two-dimensional window to obtain a coefficient matrix set, wherein the coefficient matrix set includes an approximation coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal line detail coefficient matrix.

[0139] The first determination module 840 is configured to obtain a target matrix according to a peak value region in each coefficient matrix in the coefficient matrix set, wherein the peak value region is determined according to a preset position parameter.

[0140] The processing module 850 is configured to process the target matrix by using a maximum likelihood algorithm to obtain hardware fingerprint information of the camera device.

[0141] The second determination module 860 is configured to determine the legitimacy of the camera device according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is hardware fingerprint of a legitimate device corresponding to device attribute information of the camera device.

[0142] According to the embodiment of the present disclosure, the target image information obtained by the dimension reduction sampling processing on the initial image information transmitted by the camera can be scanned and decomposed and superimposed, a coefficient matrix set corresponding to the initial image information can be obtained, a target matrix can be obtained from the peak value area of each coefficient matrix in the coefficient matrix set, and the hardware fingerprint information of the camera can be determined according to the target matrix. The legality of the camera can be determined by comparing the hardware fingerprint information with the reference fingerprint information of the corresponding legal device, thereby avoiding the situation that the illegal camera device that tampers with the device attribute information replaces the legal camera device in some places, and improving the security of image information and the like.

[0143] According to the embodiment of the present disclosure, the legality authentication device 800 further includes an acquisition module, a conversion module, an operation module and a comparison module.

[0144] The acquisition module is configured to call a data interface and acquire a plurality of device attribute information of the camera, wherein the device attribute information represents the unique identifier of the camera.

[0145] The conversion module is configured to perform format conversion and splicing processing on each device attribute information to obtain the encoding information of the camera.

[0146] The operation module is configured to perform operation processing on the encoding information based on a hash function to obtain target identification information.

[0147] The comparison module is configured to acquire image information photographed by the camera in the case that the same reference identification information as the target identification information exists in the database, wherein the reference identification information is the identification information of a legal device with the same device attribute information.

[0148] According to the embodiment of the present disclosure, the scanning and superimposing module 830 includes a decomposition unit, a superimposing unit, a merging unit and a constructing unit.

[0149] The decomposition unit is configured to perform decomposition processing on the target image information by using a plurality of two-dimensional windows to obtain a plurality of wavelet function data, wherein the two-dimensional window has the functions of stretching and shifting.

[0150] The superimposing unit is configured to superimpose the plurality of wavelet function data corresponding to each two-dimensional window to obtain a coefficient set, wherein the coefficient set includes an approximation coefficient, a horizontal detail coefficient, a vertical detail coefficient and a diagonal detail coefficient.

[0151] The merging unit is configured to obtain a coefficient matrix corresponding to any coefficient in the coefficient set according to a plurality of coefficients of a plurality of two-dimensional windows.

[0152] The constructing unit is configured to construct a coefficient matrix set according to a plurality of coefficient matrices.

[0153] According to an embodiment of the present disclosure, the first determining module 840 comprises a first determining unit, a second determining unit and a third determining unit.

[0154] The first determining unit is configured to, for each coefficient matrix, detect the coefficient matrix, and determine a main peak value and a plurality of secondary peak values of the coefficient matrix.

[0155] The second determining unit is configured to determine, based on preset position parameters, a peak value region of the main peak value and the secondary peak values respectively.

[0156] The third determining unit is configured to determine, based on the peak value region, a target matrix from the coefficient matrix.

[0157] According to an embodiment of the present disclosure, the third determining unit comprises a determining subunit, a transformation subunit, a constructing subunit and a first generating subunit.

[0158] The determining subunit is configured to, for each coefficient matrix, determine, based on the peak value region, region matrix data from the coefficient matrix.

[0159] The transformation subunit is configured to perform phase stretching transformation processing on the region matrix data to obtain an edge sharp value.

[0160] The constructing subunit is configured to construct a transition matrix according to the edge sharp value and the region matrix data.

[0161] The first generating subunit is configured to generate the target matrix according to a plurality of transition matrices.

[0162] According to an embodiment of the present disclosure, the processing module 850 comprises a second generating subunit.

[0163] The second generating subunit is configured to, in a case where the number of initial image information is one or more, generate hardware fingerprint information according to data of the target matrix corresponding to each initial image information and noise residuals of the target matrix.

[0164] According to an embodiment of the present disclosure, the second determining module 860 comprises a linear dimension reduction unit, a calculating unit, a fourth determining unit, a fifth determining unit and a sixth determining unit.

[0165] The linear dimension reduction unit is configured to, in a case where the number of initial image information is one or more, perform linear dimension reduction processing on each hardware fingerprint information by using principal component analysis to obtain one or more target fingerprint information.

[0166] The calculating unit is configured to calculate similarity between each target fingerprint information and reference fingerprint information by using a chaotic polynomial expansion method.

[0167] The fourth determining unit is configured to determine a similarity average value according to a plurality of similarities.

[0168] The fifth determination unit is configured to determine the camera device as a legal camera device if the average similarity meets a preset threshold.

[0169] The sixth determination unit is configured to determine the camera device as an illegal camera device if the average similarity does not meet the preset threshold.

[0170] According to an embodiment of the present disclosure, the downsampling module 820 comprises a cancellation unit, a smoothing unit and a dimension reduction unit.

[0171] The cancellation unit is configured to cancel irrelevant noise in the initial image information to obtain first transition image information, wherein the irrelevant noise is generated by the color filter matrix.

[0172] The smoothing unit is configured to perform smoothing processing on the first transition image information based on a minimum mean square error algorithm to obtain second transition image information.

[0173] The dimension reduction unit is configured to perform initial dimension reduction processing on the second transition image information based on a preset sampling rule to obtain target image information.

[0174] According to an embodiment of the present disclosure, the dimension reduction unit comprises a scanning subunit, a processing subunit and a third generation subunit.

[0175] The scanning subunit is configured to perform scanning processing on the second transition image using a preset sampling rule to obtain a plurality of window data, wherein the preset sampling rule comprises scanning with an m×k sampling window with a horizontal step length m and a vertical step length k.

[0176] The processing subunit is configured to, for each window data, process the window data using a maximum sampling function to obtain a plurality of dimension reduction image information.

[0177] The third generation subunit is configured to generate target image information according to the plurality of dimension reduction image information.

[0178] Any of the modules, units, sub-units, or at least part of any of them according to embodiments of the present disclosure can be implemented in one module. Any of the modules, units, sub-units according to embodiments of the present disclosure can be split into a plurality of modules. Any of the modules, units, sub-units according to embodiments of the present disclosure can be implemented at least in part as a hardware circuit, for example, a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System on Chip, a System on Substrate, a System on Package, an Application Specific Integrated Circuit (ASIC), or any other reasonable way of integrating or packaging a circuit, or in hardware or firmware, or in any one or a suitable combination of software, hardware and firmware. Alternatively, one or more of the modules, units, sub-units according to embodiments of the present disclosure can be implemented at least in part as computer program modules that can perform corresponding functions when the computer program modules are run.

[0179] For example, any of the modules 810, 820, 830, 840, 850, and 860 can be combined in one module / unit / sub-unit, or any of them can be split into a plurality of modules / units / sub-units. Alternatively, at least part of the functions of one or more of the modules / units / sub-units can be combined with at least part of the functions of the other modules / units / sub-units, and implemented in one module / unit / sub-unit. According to embodiments of the present disclosure, at least one of the modules 810, 820, 830, 840, 850, and 860 can be implemented at least in part as a hardware circuit, for example, a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System on Chip, a System on Substrate, a System on Package, an Application Specific Integrated Circuit (ASIC), or any other reasonable way of integrating or packaging a circuit, or in hardware or firmware, or in any one or a suitable combination of software, hardware and firmware. Alternatively, at least one of the modules 810, 820, 830, 840, 850, and 860 can be implemented at least in part as computer program modules that can perform corresponding functions when the computer program modules are run.

[0180] It should be noted that the legitimacy authentication device part in the embodiments of this disclosure corresponds to the legitimacy authentication method part in the embodiments of this disclosure. The description of the legitimacy authentication device part is specifically referred to in the legitimacy authentication method part, and will not be repeated here.

[0181] Figure 9 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0182] like Figure 9 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 902 or a program loaded from storage portion 908 into random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0183] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0184] According to an embodiment of the present disclosure, the electronic device 900 can further include an input / output (I / O) interface 905 that is also connected to the bus 904. The system 900 can further include one or more of the following components connected to the I / O interface 905: an input part 906 including a keyboard, a mouse, etc.; an output part 907 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 908 including a hard disk, etc.; and a communication part 909 including a network interface card such as a LAN card, a modem, etc. The communication part 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 910 as necessary, so that a computer program read therefrom is installed in the storage part 908 as necessary.

[0185] According to an embodiment of the present disclosure, the method flow according to the embodiments of the present disclosure can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable storage medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system implementing the embodiments of the present disclosure are performed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0186] The present disclosure also provides a computer-readable storage medium, which can be included in the device / apparatus / system described in the above embodiments; or can exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, which when executed, implement the methods according to the embodiments of the present disclosure.

[0187] According to an embodiment of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium. For example, it can include, but is not limited to, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM or Flash memory), a portable compact disc read only memory (CD-ROM), an optical memory device, a magnetic memory device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0188] For example, according to an embodiment of the present disclosure, the computer readable storage medium can include one or more memories described above as the ROM 902 and / or the RAM 903 and / or one or more memories other than the ROM 902 and the RAM 903.

[0189] Embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the method provided by the embodiments of the present disclosure, and when the computer program product is run on an electronic device, the program codes are used to make the electronic device implement the legality authentication method provided by the embodiments of the present disclosure.

[0190] When the computer program is executed by the processor 901, the above-mentioned functions defined in the system / apparatus of the embodiments of the present disclosure are performed. According to an embodiment of the present disclosure, the above-mentioned system, apparatus, module, unit, etc. can be implemented by computer program modules.

[0191] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal over a network medium, and be downloaded and installed through the communication part 909, and / or be installed from the detachable medium 911. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any suitable combination of the foregoing.

[0192] According to embodiments of the present disclosure, program code of the computer program for performing the methods provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and can be executed entirely on the user's computing device, partly on the user's computing device and partly on a remote computing device, or entirely on a remote computing device or server. In the case of execution partly on the user's computing device and partly on a remote computing device, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, through an Internet service provider through the Internet).

[0193] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a portion of code, which comprises one or more executable instructions for implementing the specific logical functions specified for the block. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or in the reverse order, depending on the functionality involved. It is also noted that each block of the block diagrams or flow diagrams and combinations of blocks in the block diagrams or flow diagrams can be implemented by special-purpose hardware-based systems which perform the specified functions or operations, or combinations of special-purpose hardware and computer instructions.

[0194] The above describes embodiments of the present disclosure. However, these embodiments are merely for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications shall fall within the scope of the present disclosure.

Claims

1. A legality authentication method of a camera device, comprising: acquiring initial image information transmitted by the camera device after connecting with a server; performing dimension reduction sampling processing on the initial image information to obtain target image information; scanning the target image information using a plurality of two-dimensional windows, and performing decomposition and superposition processing on information in each of the two-dimensional windows to obtain a coefficient matrix set, wherein the coefficient matrix set comprises an approximate coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal line detail coefficient matrix; obtaining a target matrix according to a peak value region in each coefficient matrix in the coefficient matrix set, wherein the peak value region is determined according to a preset position parameter, comprising: for each of the coefficient matrices, determining a region matrix data from the coefficient matrix based on the peak value region; performing phase stretching transformation processing on the region matrix data to obtain an edge sharpness value; constructing a transition matrix according to the edge sharpness value and the region matrix data; generating the target matrix according to a plurality of the transition matrices; processing the target matrix using a maximum likelihood algorithm to obtain hardware fingerprint information of the camera device, comprising: in a case where the number of the initial image information is one or more, generating the hardware fingerprint information according to data of the target matrix corresponding to each of the initial image information and noise residuals of the target matrix; determining legality of the camera device according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is a hardware fingerprint of a legal device corresponding to device attribute information of the camera device.

2. The method of claim 1, wherein, Before acquiring the image information, further comprising: calling a data interface to acquire a plurality of device attribute information of the camera device, wherein the device attribute information represents a unique identifier of the camera device; performing format conversion and splicing processing on each of the device attribute information to obtain encoding information of the camera device; performing operation processing on the encoding information based on a hash function to obtain target identification information; in a case where reference identification information identical to the target identification information exists in a database, acquiring the image information photographed by the camera device, wherein the reference identification information is identification information of a legal device identical to the device attribute information.

3. The method of claim 1, wherein, The scanning of the target image information using a plurality of two-dimensional windows and the decomposition and superposition processing on information in each of the two-dimensional windows to obtain a coefficient matrix set, comprising: performing decomposition processing on the target image information using a plurality of the two-dimensional windows to obtain a plurality of wavelet function data, wherein the two-dimensional window has the functions of stretching and shifting; for each of the two-dimensional windows, superimposing a plurality of the wavelet function data corresponding to the two-dimensional window to obtain a coefficient set, wherein the coefficient set comprises an approximate coefficient, a horizontal detail coefficient, a vertical detail coefficient, and a diagonal line detail coefficient; for any coefficient in the coefficient set, obtaining the coefficient matrix corresponding to the coefficient according to a plurality of the coefficients of a plurality of the two-dimensional windows. The coefficient matrix set is constructed according to a plurality of the coefficient matrices.

4. The method of claim 3, wherein, The target matrix is obtained according to a peak value region in each of the coefficient matrices in the coefficient matrix set, including: For each of the coefficient matrices, the coefficient matrix is detected to determine a main peak value and a plurality of secondary peak values of the coefficient matrix; Based on the preset position parameters, the peak value regions of the main peak value and the secondary peak values are respectively determined; Based on the peak value regions, the target matrix is determined from the coefficient matrix.

5. The method of claim 1, wherein, The hardware fingerprint information As shown in equation (1): （1） wherein, a number of pieces of initial image information, data characterizing the target matrix, a response non-uniformity noise residual extracted from the target matrix, i characterizes the i-th initial image information.

6. The method of claim 1, wherein, The legality of the camera device is determined according to the hardware fingerprint information and reference fingerprint information, including: In the case that the number of the initial image information is one or more, linear dimension reduction processing is performed on each of the hardware fingerprint information by using principal component analysis to obtain one or more target fingerprint information; The similarity between each of the target fingerprint information and the reference fingerprint information is calculated by using a chaotic polynomial expansion method; The similarity average value is determined according to a plurality of the similarities; In the case that the similarity average value meets a preset threshold value, the camera device is determined as a legal camera device; In the case that the similarity average value does not meet the preset threshold value, the camera device is determined as an illegal camera device.

7. The method of claim 1, wherein, The dimension reduction sampling processing is performed on the initial image information to obtain target image information, including: Irrelevant noise in the initial image information is eliminated to obtain first transition image information, wherein the irrelevant noise is generated by a color filter matrix; The first transition image information is smoothed based on a minimum mean square error algorithm to obtain second transition image information; The second transition image information is subjected to initial dimension reduction processing based on a preset sampling rule to obtain the target image information.

8. The method of claim 7, wherein, The initial dimension reduction processing is performed on the second transition image information based on a preset sampling rule to obtain the target image information, including: The second transition image is scanned using a preset sampling rule to obtain a plurality of window data, wherein the preset sampling rule comprises scanning the sampling window in a horizontal step m and a vertical step k ​ For each of the window data, the window data is processed by using a maximum sampling function to obtain a plurality of dimension reduction image information; The target image information is generated according to a plurality of the dimension reduction image information. 9.A legality authentication device of a camera device, including: A collection module configured to collect initial image information transmitted by the camera device after the camera device is connected to a server; A dimension reduction sampling module configured to perform dimension reduction sampling processing on the initial image information to obtain target image information; A scanning superposition module configured to scan the target image information by using a plurality of two-dimensional windows, and perform decomposition superposition processing on information in each of the two-dimensional windows to obtain a coefficient matrix set, wherein the coefficient matrix set includes an approximate coefficient matrix, a horizontal detail coefficient matrix, a vertical detail coefficient matrix, and a diagonal detail coefficient matrix; A first determination module configured to obtain a target matrix according to a peak value region in each of the coefficient matrices in the coefficient matrix set, wherein the peak value region is determined according to preset position parameters, including: A determination subunit configured to determine, for each of the coefficient matrices, a region matrix data from the coefficient matrix based on the peak value region; A transformation subunit configured to perform phase stretching transformation processing on the region matrix data to obtain an edge sharp value; The constructing subunit is configured to construct a transition matrix according to the edge sharpness and the region matrix data; The first generating subunit is configured to generate a target matrix according to a plurality of transition matrices; The processing module is configured to process the target matrix by using a maximum likelihood algorithm to obtain the hardware fingerprint information of the camera device, including: The second generating subunit is configured to generate the hardware fingerprint information according to the data of the target matrix corresponding to each initial image information and the noise residual error of the target matrix in a case where the number of initial image information is one or more. The second determining module is configured to determine the legality of the camera device according to the hardware fingerprint information and reference fingerprint information, wherein the reference fingerprint information is the hardware fingerprint of a legal device corresponding to the device attribute information of the camera device.

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