Digital human identity verification method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a digital human identity verification method and device, electronic equipment and a storage medium. According to the specific implementation scheme, user behavior information is obtained, and virtual environment information using the digital human is obtained; according to the user behavior information and the virtual environment information, determining a risk level of a virtual environment where the digital human is located; executing a corresponding identity verification strategy according to the risk level; the identity verification strategy comprises at least one of basic information verification, biological characteristic verification and equipment dynamic verification. Different identity authentication strategies are executed for different risk levels, the dynamic change of user behaviors and the change of the virtual environment can be effectively combined, the authentication mode can be flexibly adjusted, and the user experience is improved on the premise of ensuring the security of the digital human system.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a digital human identity authentication method, device, electronic device and storage medium. Background Art

[0002] With the development of digital human technology, the problem of digital human identity authentication in virtual environments has become increasingly prominent. Traditional basic information verification methods, such as username and password verification, are not only easy to be cracked, but also have the risk of identity theft and data leakage. Traditional biometric verification methods do not take different verification strategies into account the risk level of the virtual environment in which the digital human is located. Either the verification method is single and the security is insufficient, or the verification steps are too cumbersome, resulting in a poor user experience. Summary of the invention

[0003] The present invention provides a digital human identity authentication method, device, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, a digital human identity verification method is provided, comprising:

[0005] Obtain user behavior information and obtain virtual environment information using digital humans;

[0006] Determining the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information;

[0007] Execute a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

[0008] According to another aspect of the present disclosure, there is provided a digital human identity verification device, comprising:

[0009] An information acquisition module, configured to acquire user behavior information and virtual environment information of the digital human being;

[0010] A risk determination module, configured to determine the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information;

[0011] The identity authentication module is configured to execute a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

[0012] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method in any of the above technical solutions.

[0016] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods described in the above technical solutions.

[0017] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements any one of the methods described in the above technical solutions when executed by a processor.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0020] Figure 1 is a schematic diagram of the steps of the digital human identity authentication method in the embodiment of the present disclosure;

[0021] Figure 2 is a principle block diagram of a digital human identity verification device in an embodiment of the present disclosure;

[0022] Figure 3 It is a block diagram of an electronic device used to implement the digital human identity authentication method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] In view of the technical problems that the digital human identity authentication security in the prior art is insufficient and the authentication steps are too complicated, the present disclosure provides a digital human identity authentication method, such as Figure 1 As shown, including:

[0025] Step S101, obtaining user behavior information and obtaining virtual environment information of the digital human.

[0026] Specifically, user behavior information may include the user’s operation behavior on the digital human platform, such as logging in, using digital humans, etc., as well as the interactive behavior of digital humans in the virtual environment, and other information that can reflect user behavior. Virtual environment information may include but is not limited to the network environment information of the digital human, such as IP address, network status, virtual assets, virtual permissions, and other information that can reflect changes in the virtual environment.

[0027] Step S102, determining the risk level of the virtual environment in which the digital human is located according to the user behavior information and the virtual environment information.

[0028] In this embodiment, multiple risk levels can be pre-set, such as low risk, medium risk, and high risk. More risk levels can be set according to actual application scenarios. The current risk level of the digital human can be determined by combining the user's interactive operations on the human-computer interaction interface, the interactive behavior displayed by the digital human, and whether the various parameters of the virtual environment are abnormal.

[0029] Step S103, executing a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

[0030] Specifically, different risk levels require different identity authentication strategies. When the virtual environment is at a low risk level, a simpler authentication method can be used, for example, any one of the basic account secret authentication, face recognition, fingerprint recognition, iris recognition, voiceprint recognition, palmprint recognition, and dynamic verification code recognition can be used to avoid the verification steps being too cumbersome and increasing the burden on users. When the virtual environment is at a high risk level, a more complex combination of authentication methods can be used, for example, face recognition, fingerprint recognition, voiceprint recognition, and additional dynamic verification codes can be combined for identity authentication to ensure that the digital human usage environment is safe. This can effectively combine the dynamic changes in user behavior and changes in the virtual environment, flexibly adjust the authentication method, and improve the user experience while ensuring the security of the digital human system.

[0031] As an optional implementation, step S101, obtaining user behavior information includes:

[0032] The user login behavior information is obtained as the user behavior information.

[0033] In the first embodiment of the present disclosure, only the user login behavior information can be obtained as the user behavior information, such as whether the user login frequency is abnormal, whether there is a login behavior that is significantly higher than the previous login frequency. After obtaining the user login behavior information, the virtual environment information of the digital human, such as network environment information, user login device and other information, can be combined to determine the risk level of the current digital human.

[0034] As an optional implementation, step S101, obtaining user behavior information includes:

[0035] The virtual behavior information of the digital human in the virtual environment is obtained as user behavior information.

[0036] Specifically, in the second embodiment of the present disclosure, only the virtual behavior of the digital person in the virtual environment can be obtained as the user behavior information, for example, whether the speech and actions of the digital person in the virtual environment are obviously abnormal, and whether the external behavior displayed by the digital person is obviously different from the previous behavior. The risk level of the current digital person can be determined by combining the virtual behavior of the digital person and the virtual environment information.

[0037] As an optional implementation, step S101, obtaining user behavior information includes:

[0038] The user login behavior information is obtained as the virtual behavior information of the digital person in the virtual environment, and as the user behavior information.

[0039] Specifically, in the third embodiment of the present disclosure, the user's behavior of logging into the digital human system can be obtained, and the virtual behavior of the digital human in the virtual environment can also be obtained synchronously as user behavior information. In this embodiment, the risk level of the current digital human can be determined by combining the user's login behavior, the digital human's virtual behavior and the virtual environment information.

[0040] As an optional implementation, the user login behavior information includes at least one of the following: user login frequency; user login time; user login location; user login duration; user login device.

[0041] Specifically, any one or more of the user login frequency, user login time, user login location, and user login duration can be obtained to determine whether the user login behavior is abnormal. For example, if the user usually logs in from Beijing and suddenly changes to Shanghai or overseas, then it can be determined that the user login behavior is obviously abnormal. For example, if the user logs in more frequently than before, or logs in at an unusual time, these can be considered abnormal login behaviors.

[0042] As an optional implementation, the virtual behavior information includes at least one of the following: the speech of the digital person in the virtual environment; the actions of the digital person in the virtual environment; and the interaction frequency of the digital person in the virtual environment.

[0043] Specifically, the virtual behavior information of digital people includes but is not limited to speech, action, and interaction frequency, where the speech of digital people may include voice interaction and text interaction, and the action may include body movements, gestures, facial expressions, etc. It is possible to compare whether the speech and actions of digital people are significantly different from the past. Specifically, an artificial intelligence model can be used to extract and identify the speech characteristics and action characteristics of digital people, and compare them with historical speeches and actions to determine whether the actions and speeches are obviously abnormal. It is also possible to compare whether the interaction frequency in the current period is significantly higher than the previous interaction frequency. Combined with the different behaviors of digital people in the virtual environment, it is possible to comprehensively judge whether the behavior of digital people is abnormal, thereby improving the accuracy of risk judgment.

[0044] As an optional implementation, the virtual environment information includes at least one of the following: network environment information; user login device; virtual authority information; virtual asset information; digital human appearance feature information.

[0045] Specifically, network environment information may include IP address, network status, etc. Digital human appearance feature information may include digital human appearance, decoration style, etc. If the digital human suddenly undergoes significant changes in appearance (such as frequent changes in appearance or clothing), or the appearance style is significantly different from the usual appearance style, the risk score will also be increased.

[0046] As an optional implementation, step S102, determining the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information, includes:

[0047] If there are no abnormalities in the user behavior information and the virtual environment information, the virtual environment is determined to be at a low risk level.

[0048] If at least one of the user behavior information and the virtual environment information is abnormal, the virtual environment is determined to be at a medium risk level.

[0049] If both the user behavior information and the virtual environment information are abnormal, the virtual environment is determined to be at a high risk level.

[0050] Specifically, the user's behavior can be analyzed based on the user behavior information through the artificial intelligence model, and each user's behavior can be scored. If there is an abnormality, the corresponding risk score will be increased; similarly, each parameter in the virtual environment information can be scored, and if the parameters of the virtual environment information are abnormal, the corresponding risk score will be increased. Finally, a total risk score is obtained based on the risk scores of the user behavior information and the virtual environment information. Taking the percentage system as an example, assuming that the total risk score is between 0 and 30 points, it can be determined as low risk; the total risk score is between 31 and 60 points, it can be determined as medium risk; the total risk score is between 61 and 100 points, it can be determined as high risk.

[0051] As an optional implementation, step S103, executing a corresponding identity authentication strategy according to the risk level includes:

[0052] In response to the virtual environment being at a low risk level, the identity authentication strategy is to select a verification method from basic information verification, biometric verification, and device dynamic verification.

[0053] Specifically, when the virtual environment is at a low risk level, a relatively simple single-mode verification can be used, for example, basic information verification (account secret verification), or one of the biometric verifications such as face recognition, fingerprint recognition, iris recognition, voiceprint recognition, palmprint recognition, or dynamic verification code verification, or external device verification. In this embodiment, in order to simplify the user's operation, any one of the biometric verifications such as face recognition, fingerprint recognition, iris recognition, voiceprint recognition, and palmprint recognition can be used for low-risk environments to avoid the verification steps being too cumbersome and increase the burden on users, thereby improving the user experience.

[0054] In response to the virtual environment being at a medium risk level, the identity authentication strategy is to select two verification methods from basic information verification, biometric verification, and device dynamic verification for combined verification.

[0055] Specifically, when the virtual environment is at a medium risk level, a dual-modal combination verification method can be used, for example, combining face recognition and voiceprint recognition for verification. This method does not require additional user operations. Although voiceprint recognition is added, the user may not be aware of it. Or face recognition combined with dynamic verification code for identity authentication. The dynamic verification code verification method is more secure and ensures that the digital human usage environment is safe. Under the premise of ensuring the safety of the usage environment, the user experience should not be affected as much as possible.

[0056] In response to the virtual environment being at a high risk level, the identity authentication strategy is to select two or more verification methods from basic information verification, biometric verification, and device dynamic verification for combined verification.

[0057] Specifically, when the virtual environment is at a high risk level, a more complex multi-modal combination verification method can be used, for example, combining face recognition, fingerprint recognition, voiceprint recognition, and additional dynamic verification codes or hardware device verification to authenticate the identity and ensure that the digital human usage environment is safe. This can effectively combine the dynamic changes in user behavior and changes in the virtual environment, flexibly adjust the verification method, and improve the user experience while ensuring the security of the digital human system.

[0058] As an optional implementation, the biometric verification includes at least one of the following: fingerprint verification; palm print verification; iris verification; facial feature verification; voice print verification. In addition to these biometric verifications, other biometric features may also be used, such as palm vein, retina verification, etc.

[0059] Specifically, fingerprint recognition is the rapid collection of user's fingerprint features through a high-precision fingerprint sensor. Facial recognition uses a 3D depth camera to capture the user's facial features, and uses AI algorithms for real-time analysis and matching. Voiceprint recognition collects the user's voiceprint data through a microphone, combined with natural language processing technology to improve recognition accuracy. Iris recognition uses a high-resolution camera to obtain iris images as a supplementary means of multimodal verification. This integration of multimodal biometrics not only improves the accuracy and security of identity authentication, but also provides users with a variety of verification options and enhances the flexibility of the system. In addition, the storage and processing of biometric data uses distributed encryption technology to ensure the security of data during transmission and storage. The present disclosure introduces zero-knowledge proof technology, which does not disclose specific biometric information while verifying the user's identity, thereby maximizing the protection of user privacy.

[0060] The present disclosure also provides a digital human identity verification device 200, comprising:

[0061] The information acquisition module 201 is configured to acquire user behavior information and acquire virtual environment information of the digital human.

[0062] Specifically, user behavior information may include the user’s operation behavior on the digital human platform, such as logging in, using digital humans, etc., as well as the interactive behavior of digital humans in the virtual environment, and other information that can reflect user behavior. Virtual environment information may include but is not limited to the network environment information of the digital human, such as IP address, network status, virtual assets, virtual permissions, and other information that can reflect changes in the virtual environment.

[0063] The risk determination module 202 is configured to determine the risk level of the virtual environment in which the digital human is located based on the user behavior information and the virtual environment information.

[0064] In this embodiment, multiple risk levels can be pre-set, such as low risk, medium risk, and high risk. More risk levels can be set according to actual application scenarios. The current risk level of the digital human can be determined by combining the user's interactive operations on the human-computer interaction interface, the interactive behavior displayed by the digital human, and whether the various parameters of the virtual environment are abnormal.

[0065] The identity authentication module 203 is configured to execute a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

[0066] Specifically, different risk levels require different identity authentication strategies. When the virtual environment is at a low risk level, a simpler authentication method can be used, for example, any one of the basic account secret authentication, face recognition, fingerprint recognition, iris recognition, voiceprint recognition, palmprint recognition, and dynamic verification code recognition can be used to avoid the verification steps being too cumbersome and increasing the burden on users. When the virtual environment is at a high risk level, a more complex combination of authentication methods can be used, for example, face recognition, fingerprint recognition, voiceprint recognition, and additional dynamic verification codes can be combined for identity authentication to ensure that the digital human usage environment is safe. This can effectively combine the dynamic changes in user behavior and changes in the virtual environment, flexibly adjust the authentication method, and improve the user experience while ensuring the security of the digital human system.

[0067] As an optional implementation, the information acquisition module 201 acquires user behavior information including:

[0068] The user login behavior information is obtained as the user behavior information.

[0069] In the first embodiment of the present disclosure, the information acquisition module 201 can only acquire user login behavior information as user behavior information, such as whether the user login frequency is abnormal, and whether there is a login behavior that is significantly higher than the previous login frequency. After acquiring the user login behavior information, the risk level of the current digital human can be determined in combination with the virtual environment information of the digital human, such as network environment information, user login device and other information.

[0070] As an optional implementation, the information acquisition module 201 acquires user behavior information including:

[0071] The virtual behavior information of the digital human in the virtual environment is obtained as user behavior information.

[0072] Specifically, in the second embodiment of the present disclosure, the information acquisition module 201 can only acquire the virtual behavior of the digital person in the virtual environment as the user behavior information, for example, whether the speech and actions of the digital person in the virtual environment are obviously abnormal, and whether the external behavior displayed by the digital person is obviously different from the previous behavior. The risk level of the current digital person can be determined by combining the virtual behavior of the digital person and the virtual environment information.

[0073] As an optional implementation, the information acquisition module 201 acquires user behavior information including:

[0074] The user login behavior information is obtained as the virtual behavior information of the digital person in the virtual environment, and as the user behavior information.

[0075] Specifically, in the third embodiment of the present disclosure, the information acquisition module 201 can not only acquire the user's behavior of logging into the digital human system, but also simultaneously acquire the virtual behavior of the digital human in the virtual environment as user behavior information. In this embodiment, the risk level of the current digital human can be determined by combining the user's login behavior, the digital human's virtual behavior and the virtual environment information.

[0076] As an optional implementation, the user login behavior information includes at least one of the following: user login frequency; user login time; user login location; user login duration; user login device.

[0077] Specifically, any one or more of the user login frequency, user login time, user login location, and user login duration can be obtained to determine whether the user login behavior is abnormal. For example, if the user usually logs in from Beijing and suddenly changes to Shanghai or overseas, then it can be determined that the user login behavior is obviously abnormal. For example, if the user logs in more frequently than before, or logs in at an unusual time, these can be considered abnormal login behaviors.

[0078] As an optional implementation, the virtual behavior information includes at least one of the following: the speech of the digital person in the virtual environment; the actions of the digital person in the virtual environment; and the interaction frequency of the digital person in the virtual environment.

[0079] Specifically, the virtual behavior information of digital people includes but is not limited to speech, action, and interaction frequency, where the speech of digital people may include voice interaction and text interaction, and the action may include body movements, gestures, facial expressions, etc. It is possible to compare whether the speech and actions of digital people are significantly different from the past. Specifically, an artificial intelligence model can be used to extract and identify the speech characteristics and action characteristics of digital people, and compare them with historical speeches and actions to determine whether the actions and speeches are obviously abnormal. It is also possible to compare whether the interaction frequency in the current period is significantly higher than the previous interaction frequency. Combined with the different behaviors of digital people in the virtual environment, it is possible to comprehensively judge whether the behavior of digital people is abnormal, thereby improving the accuracy of risk judgment.

[0080] As an optional implementation, the virtual environment information includes at least one of the following: network environment information; user login device; virtual authority information; virtual asset information; digital human appearance feature information.

[0081] Specifically, network environment information may include IP address, network status, etc. Digital human appearance feature information may include digital human appearance, decoration style, etc. If the digital human suddenly undergoes significant changes in appearance (such as frequent changes in appearance or clothing), or the appearance style is significantly different from the usual appearance style, the risk score will also be increased.

[0082] As an optional implementation, the risk determination module 202 determines the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information, including:

[0083] If there are no abnormalities in the user behavior information and the virtual environment information, the virtual environment is determined to be at a low risk level.

[0084] If at least one of the user behavior information and the virtual environment information is abnormal, the virtual environment is determined to be at a medium risk level.

[0085] If both the user behavior information and the virtual environment information are abnormal, the virtual environment is determined to be at a high risk level.

[0086] Specifically, the user's behavior can be analyzed based on the user behavior information through the artificial intelligence model, and each user's behavior can be scored. If there is an abnormality, the corresponding risk score will be increased; similarly, each parameter in the virtual environment information can be scored, and if the parameters of the virtual environment information are abnormal, the corresponding risk score will be increased. Finally, a total risk score is obtained based on the risk scores of the user behavior information and the virtual environment information. Taking the percentage system as an example, assuming that the total risk score is between 0 and 30 points, it can be determined as low risk; the total risk score is between 31 and 60 points, it can be determined as medium risk; the total risk score is between 61 and 100 points, it can be determined as high risk.

[0087] As an optional implementation, the identity authentication module 203 executes the corresponding identity authentication strategy according to the risk level, including:

[0088] In response to the virtual environment being at a low risk level, the identity authentication strategy is to select a verification method from basic information verification, biometric verification, and device dynamic verification.

[0089] Specifically, when the virtual environment is at a low risk level, a relatively simple single-mode verification can be used, for example, basic information verification (account secret verification), or one of the biometric verifications such as face recognition, fingerprint recognition, iris recognition, voiceprint recognition, palmprint recognition, or dynamic verification code verification, or external device verification. In this embodiment, in order to simplify the user's operation, any one of the biometric verifications such as face recognition, fingerprint recognition, iris recognition, voiceprint recognition, and palmprint recognition can be used for low-risk environments to avoid the verification steps being too cumbersome and increase the burden on users, thereby improving the user experience.

[0090] In response to the virtual environment being at a medium risk level, the identity authentication strategy is to select two verification methods from basic information verification, biometric verification, and device dynamic verification for combined verification.

[0091] Specifically, when the virtual environment is at a medium risk level, a dual-modal combination verification method can be used, for example, combining face recognition and voiceprint recognition for verification. This method does not require additional user operations. Although voiceprint recognition is added, the user may not be aware of it. Or face recognition combined with dynamic verification code for identity authentication. The dynamic verification code verification method is more secure and ensures that the digital human usage environment is safe. Under the premise of ensuring the safety of the usage environment, the user experience should not be affected as much as possible.

[0092] In response to the virtual environment being at a high risk level, the identity authentication strategy is to select two or more verification methods from basic information verification, biometric verification, and device dynamic verification for combined verification.

[0093] Specifically, when the virtual environment is at a high risk level, a more complex multi-modal combination verification method can be used, for example, combining face recognition, fingerprint recognition, voiceprint recognition, and additional dynamic verification codes or hardware device verification to authenticate the identity and ensure that the digital human usage environment is safe. This can effectively combine the dynamic changes in user behavior and changes in the virtual environment, flexibly adjust the verification method, and improve the user experience while ensuring the security of the digital human system.

[0094] As an optional implementation, the biometric verification includes at least one of the following: fingerprint verification; palm print verification; iris verification; facial feature verification; voice print verification. In addition to these biometric verifications, other biometric features may also be used, such as palm vein, retina verification, etc.

[0095] Specifically, fingerprint recognition is to quickly collect the user's fingerprint features through a high-precision fingerprint sensor. Facial recognition uses a 3D depth camera to capture the user's facial features and uses AI algorithms for real-time analysis and matching. Voiceprint recognition collects the user's voiceprint data through a microphone and combines it with natural language processing technology to improve recognition accuracy. Iris recognition uses a high-resolution camera to obtain iris images as a supplementary means of multimodal verification. This integration of multimodal biometrics not only improves the accuracy and security of identity authentication, but also provides users with a variety of verification options and enhances the flexibility of the system.

[0096] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0097] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0098] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0099] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0100] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0101] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as a digital human identity authentication method. For example, in some embodiments, the digital human identity authentication method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the digital human identity authentication method described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the digital human identity authentication method in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0107] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0109] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A digital human identity verification method, comprising: Obtain user behavior information and obtain virtual environment information using digital humans; Determining the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information; Execute a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

2. The method according to claim 1, wherein: The obtaining of user behavior information includes: User login behavior information is obtained as the user behavior information.

3. The method according to claim 1, wherein: The obtaining of user behavior information includes: The virtual behavior information of the digital human in the virtual environment is obtained as the user behavior information.

4. The method according to claim 1, wherein: The obtaining of user behavior information includes: The user login behavior information is obtained as the virtual behavior information of the digital human in the virtual environment, and as the user behavior information.

5. The method according to claim 2 or 4, wherein: The user login behavior information includes at least one of the following: user login frequency; user login location; user login time; user login duration; user login device.

6. The method according to claim 3 or 4, wherein: The virtual behavior information includes at least one of the following: the speech of the digital person in the virtual environment; the actions of the digital person in the virtual environment; and the interaction frequency of the digital person in the virtual environment.

7. The method according to claim 1, wherein: The virtual environment information includes at least one of the following: network environment information; virtual authority information; virtual asset information; digital human appearance feature information.

8. The method according to any one of claims 1 to 7, wherein: Determining the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information includes: If neither the user behavior information nor the virtual environment information is abnormal, determining that the virtual environment is at a low risk level; If at least one of the user behavior information and the virtual environment information is abnormal, determining that the virtual environment is at a medium risk level; If both the user behavior information and the virtual environment information are abnormal, it is determined that the virtual environment is at a high risk level.

9. The method according to claim 8, wherein: The executing of the corresponding identity authentication strategy according to the risk level includes: In response to the virtual environment being at a low risk level, the identity authentication strategy is to select a verification method from the basic information verification, the biometric verification, and the device dynamic verification; In response to the virtual environment being at a medium risk level, the identity authentication strategy is to select two authentication methods from the basic information authentication, the biometric authentication, and the device dynamic authentication for combined authentication; In response to the virtual environment being at a high risk level, the identity authentication strategy is to select two or more authentication methods from among the basic information authentication, the biometric authentication, and the device dynamic authentication for combined authentication.

10. The method according to claim 9, wherein: The biometric verification includes at least one of the following: fingerprint verification; palm print verification; iris verification; facial feature verification; voice print verification.

11. A digital human identity verification device, comprising: An information acquisition module, configured to acquire user behavior information and virtual environment information of the digital human being; A risk determination module, configured to determine the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information; The identity authentication module is configured to execute a corresponding identity authentication strategy according to the risk level; wherein the identity authentication strategy includes at least one of basic information verification, biometric verification, and device dynamic verification.

12. The device according to claim 11, wherein The information acquisition module acquires user behavior information including: User login behavior information is obtained as the user behavior information.

13. The device according to claim 11, wherein: The information acquisition module acquires user behavior information including: The virtual behavior information of the digital human in the virtual environment is obtained as the user behavior information.

14. The device according to claim 11, wherein: The information acquisition module acquires user behavior information including: The user login behavior information is obtained as the virtual behavior information of the digital human in the virtual environment, and as the user behavior information.

15. The device according to claim 12 or 14, wherein: The user login behavior information includes at least one of the following: user login frequency; user login time; user login duration; user login device.

16. The device according to claim 13 or 14, wherein: The virtual behavior information includes at least one of the following: the speech of the digital person in the virtual environment; the actions of the digital person in the virtual environment; and the interaction frequency of the digital person in the virtual environment.

17. The device according to claim 11, wherein: The virtual environment information includes at least one of the following: network environment information; user login location; virtual authority information; virtual asset information; digital human appearance feature information.

18. The device according to any one of claims 11 to 17, wherein: The risk determination module determines the risk level of the virtual environment where the digital human is located according to the user behavior information and the virtual environment information, including: If neither the user behavior information nor the virtual environment information is abnormal, determining that the virtual environment is at a low risk level; If at least one of the user behavior information and the virtual environment information is abnormal, determining that the virtual environment is at a medium risk level; If both the user behavior information and the virtual environment information are abnormal, it is determined that the virtual environment is at a high risk level.

19. The device according to claim 18, wherein: The identity authentication module executes the corresponding identity authentication strategy according to the risk level, including: In response to the virtual environment being at a low risk level, the identity authentication strategy is to select a verification method from the basic information verification, the biometric verification, and the device dynamic verification; In response to the virtual environment being at a medium risk level, the identity authentication strategy is to select two authentication methods from the basic information authentication, the biometric authentication, and the device dynamic authentication for combined authentication; In response to the virtual environment being at a high risk level, the identity authentication strategy is to select two or more authentication methods from among the basic information authentication, the biometric authentication, and the device dynamic authentication for combined authentication.

20. The device according to claim 19, wherein The biometric verification includes at least one of the following: fingerprint verification; palm print verification; iris verification; facial feature verification; voice print verification.

21. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-10.

23. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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