CN107958215A - A kind of antifraud recognition methods, device, server and storage medium - Google Patents

Abstract

The embodiment of the invention discloses a kind of antifraud recognition methods, device, server and storage medium, this method includes：When receiving the voice collecting instruction that user to be identified sends, the current speech signal that the user to be identified inputs according to pre-set input mode is gathered；Extract the current vocal print feature parameter in the current speech signal；Wherein, the current vocal print feature parameter includes：Oral cavity characteristic parameter, nasal sound characteristic parameter, the flat tongue characteristic parameter of volume or vocal cords characteristic parameter；When the current vocal print feature parameter is matched with the fraud vocal print feature parameter pre-saved, the corresponding current frequency value of the current vocal print feature parameter is obtained in the database；When the current frequency value is more than pre-set frequency threshold, the user to be identified is identified as fraudulent user.The embodiment of the present invention provides a kind of antifraud recognition methods, device, server and storage medium, it can be achieved that improving the accuracy of identification fraud.

Description

Anti-fraud identification method, device, server and storage medium

technical field

The embodiments of the present invention relate to the technical field of network fraud, and in particular to an anti-fraud identification method, device, server and storage medium.

Background technique

With the development of science and technology, the application of e-commerce has become more and more extensive, and the confidentiality, integrity and security of information have been greatly tested.

In the existing fraud identification methods, at present, the identity of the user is mainly verified by recognizing the face or proofreading the basic information of the user. However, the two methods for verifying the user's identity provided by the existing technology have defects. Among them, the counterfeit applicant in the recognition of the face uses the 3D software modeling technology to prepare the user's "mask face". , it is easy to misjudgment, resulting in the success of face recognition; or a counterfeit applicant can steal the basic information of the user, such as: user name, family information and work information, etc., can pretend to be the user to apply for credit, potentially making the basic information of the user proofread success. Therefore, the existing technology has low accuracy in verifying fraudulent behaviors of user identification, is prone to misjudgment, and easily threatens the security of the user's property.

Contents of the invention

Embodiments of the present invention provide an anti-fraud identification method, device, server, and storage medium, so as to improve the accuracy of fraud identification.

In a first aspect, an embodiment of the present invention provides an anti-fraud identification method, the method comprising:

When receiving the voice collection instruction sent by the user to be identified, collect the current voice signal input by the user to be identified according to the preset input mode;

Extracting the current voiceprint feature parameters in the current voice signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters or vocal cord feature parameters;

When the current voiceprint characteristic parameter matches the pre-saved fraudulent voiceprint characteristic parameter, obtain the current frequency value corresponding to the current voiceprint characteristic parameter in the database;

When the current frequency value is greater than a preset frequency threshold, the user to be identified is identified as a fraudulent user.

In the second aspect, the embodiment of the present invention also provides an anti-fraud identification device, which includes:

Receiving module, extracting module, acquiring module and identifying module; wherein,

The receiving module is configured to collect a current voice signal input by the user to be identified according to a preset input mode when receiving a voice collection instruction sent by the user to be identified;

The extraction module is used to extract the current voiceprint characteristic parameters in the current speech signal; wherein, the current voiceprint characteristic parameters include: oral cavity characteristic parameters, nasal sound characteristic parameters, tongue rolling and flattening characteristic parameters or vocal cord characteristic parameters;

The acquisition module is configured to acquire the current frequency value corresponding to the current voiceprint characteristic parameter in the database when the current voiceprint characteristic parameter matches the pre-saved fraudulent voiceprint characteristic parameter;

The identification module is configured to identify the user to be identified as a fraudulent user when the current frequency value is greater than a preset frequency threshold.

In a third aspect, an embodiment of the present invention also provides a server, where the server includes:

one or more processors;

storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors are made to implement the anti-fraud identification method as described above.

In a fourth aspect, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the anti-fraud identification method as described above is implemented.

The embodiment of the present invention collects the voice signal pre-input by the user to be identified, matches the characteristic parameter of the fraudulent voiceprint by extracting the current voiceprint feature parameter in the current voice signal, and obtains the current frequency value corresponding to the current voiceprint feature parameter, If the current frequency value is greater than the preset frequency threshold, it is determined that the user to be identified is a fraudulent user, which solves the problem of easy misjudgment when identifying fraudulent behavior in the prior art, improves the accuracy of identifying fraudulent behavior, and further ensures the user's security. Property security.

Description of drawings

FIG. 1 is a flow chart of the anti-fraud identification method in Embodiment 1 of the present invention;

Fig. 2 is a flowchart of the anti-fraud identification method in Embodiment 2 of the present invention;

Fig. 3 is a schematic structural diagram of the anti-fraud device in Embodiment 3 of the present invention;

FIG. 4 is a schematic structural diagram of a server in Embodiment 4 of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings but not all structures.

Embodiment one

Figure 1 is a flow chart of the anti-fraud identification method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of identifying the illegal behavior of fraudulent users. The method can be executed by an anti-fraud identification device, which can adopt implemented in software and/or hardware. As shown in Figure 1, the method specifically includes:

S110. When receiving a voice collection instruction sent by the user to be identified, collect a current voice signal input by the user to be identified according to a preset input mode.

In this embodiment, in order to accurately determine the fraudulent behavior, it is first necessary to receive the voice collection instruction sent by the current user to be identified. The current user to be identified can be a user applying for credit, and the user to be identified can input the current voice signal according to the preset input method. Wherein, the acquisition of the voice signal may randomly generate some password information on the server side, and display the password information to the user to be identified. Wherein, the password information can be obtained in the form of question and answer.

S120. Extract current voiceprint feature parameters in the current voice signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters, or vocal cord feature parameters.

After the current voice signal is collected, the current voiceprint feature parameters included in the current voice signal are extracted, wherein the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, tongue roll and flat tongue feature parameters or vocal cord feature parameters. Wherein, the above-mentioned feature parameters may have a judgment range value.

S130. When the current voiceprint feature parameter matches the pre-saved fraudulent voiceprint feature parameter, acquire the current frequency value corresponding to the current voiceprint feature parameter from the database.

The pre-selected and saved fraudulent voiceprint characteristic parameters include the fraudulent user's mouth characteristic parameters, nasal sound characteristic parameters, flat tongue curl characteristic parameters or vocal cord characteristic parameters and other information pre-stored in the database. Among them, the pre-selected and saved fraudulent voiceprint feature parameters are set with judgment range values. If the current voiceprint feature parameters match the pre-saved fraudulent voiceprint feature parameters, that is: the judgment range value of the current voiceprint feature parameters falls within the pre-saved If the judgment range value of the voiceprint feature parameter is cheated, the voiceprint feature parameter is considered to be matched successfully.

If the voiceprint characteristic parameters are successfully matched, the current frequency value corresponding to the current voiceprint characteristic parameter can be obtained in the database, that is, the number of occurrences of the judgment range value of the current voiceprint characteristic parameter.

S140. When the current frequency value is greater than a preset frequency threshold, identify the user to be identified as a fraudulent user.

The frequency threshold preset in the database may be greater than or equal to twice. If the frequency value of the current voiceprint characteristic parameter is greater than the preset frequency threshold, the user to be identified will be identified as a fraudulent user.

The anti-fraud identification method provided by the embodiment of the present invention collects the voice signal input by the user to be identified in advance, and then judges the current voiceprint feature by extracting the current voiceprint feature parameters in the current voice signal and matching the pre-saved fraudulent voiceprint feature parameters The current frequency value corresponding to the parameter, if the current frequency value is greater than the preset frequency threshold, it is determined that the user to be identified is a fraudulent user, which solves the problem of easy misjudgment when identifying fraudulent behavior in the prior art, and improves the accuracy of identifying fraudulent behavior Degree, to further ensure the safety of the user's property.

Optionally, collecting the current voice signal input by the user to be identified according to a preset input method includes: randomly generating password information for acquiring the current voice signal, and displaying the password information to the user to be identified; collecting the user to be identified and reading the password The current speech signal submitted after the message.

The preset input method includes the method of inputting the voice signal in the form of question and answer, and collects the current voice signal input by the user to be recognized according to the preset input method, including: randomly generating password information for obtaining the current voice signal, and the password information can include text , display the password information to the user to be identified, collect the current voice signal sent by the user to be identified by reading according to the password information, and submit and analyze the current voice signal.

In this embodiment, the current voice signal input by the user to be identified after reading the password information is convenient and fast, and can further save the time for signal collection.

Optionally, obtaining the current frequency value corresponding to the current voiceprint characteristic parameter in the database includes: obtaining the user identification of the user to be identified in the voice collection instruction; obtaining the current frequency corresponding to the current voiceprint characteristic parameter in the database according to the user identification value.

User ID is the name by which a user is identified when logging in or registering. The user identification of the user to be identified is obtained in the voice collection instruction, and the identity information of the user can be judged by the user ID. According to the user identification, the current frequency value corresponding to the current voiceprint feature parameter can be acquired in the database. The corresponding current frequency value may be zero times, or greater than or equal to one times.

In this embodiment, the current voiceprint feature parameters can be obtained through the user identification, and the corresponding current frequency value in the database can be accurately obtained according to the uniqueness of the voiceprint feature. This method can more accurately determine fraudulent users and fraudulent behavior .

Embodiment two

Fig. 2 is a flowchart of the anti-fraud identification method provided by Embodiment 2 of the present invention. This embodiment is optimized on the basis of Embodiment 1. The anti-fraud identification method provided in Embodiment 2 also includes: the current frequency value is less than or equal to When the preset frequency threshold is set, the basic information input by the user to be identified is received; when the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information, the user to be identified is identified as a fraudulent user.

S210. When receiving a voice collection instruction sent by the user to be identified, collect a current voice signal input by the user to be identified according to a preset input mode.

S220. Extract the current voiceprint feature parameters in the current speech signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters, or vocal cord feature parameters.

S230. When the current voiceprint feature parameter matches the pre-saved fraudulent voiceprint feature parameter, acquire the current frequency value corresponding to the current voiceprint feature parameter from the database.

S240. When the current frequency value is less than or equal to the preset frequency threshold, receive the basic information input by the user to be identified; when the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information, identify the user to be identified as a fraudulent user .

If the current frequency value is less than or equal to the preset frequency threshold, the basic information input by the user to be identified is received. The basic information input by the user to be identified includes family information and work information. When the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information, the user to be identified is judged as a fraudulent user. Wherein, the pre-saved basic fraud information is the basic information already stored in the database.

In this embodiment, on the basis of the voiceprint judgment, the basic information input by the user is judged again, and a judgment standard is added to improve the accuracy of judging fraudulent users.

Embodiment Three

Fig. 3 is a schematic structural diagram of the anti-fraud device in Embodiment 3 of the present invention. As shown in Figure 3, the anti-fraud device includes: a receiving module, an extraction module, an acquisition module and an identification module.

The receiving module 310 is configured to collect a current voice signal input by the user to be recognized according to a preset input mode when receiving a voice collection instruction sent by the user to be recognized.

The extraction module 320 is used to extract the current voiceprint characteristic parameters in the current voice signal; wherein, the current voiceprint characteristic parameters include: oral cavity characteristic parameters, nasal sound characteristic parameters, tongue-rolled and flattened characteristic parameters or vocal cord characteristic parameters.

The acquisition module 330 is configured to acquire the current frequency value corresponding to the current voiceprint characteristic parameter in the database when the current voiceprint characteristic parameter matches the pre-saved fraudulent voiceprint characteristic parameter.

The identifying module 340 is configured to identify the user to be identified as a fraudulent user when the current frequency value is greater than a preset frequency threshold.

Further, the receiving module 310 is specifically configured to randomly generate password information for acquiring the current voice signal, display the password information to the user to be identified; collect the current voice signal submitted by the user to be identified after reading the password information.

The obtaining module 330 is specifically configured to obtain the user identification of the user to be identified in the voice collection instruction; obtain the current frequency value corresponding to the current voiceprint feature parameter in the database according to the user identification.

The receiving module 310 is also used to receive the basic information input by the user to be identified when the current frequency value is less than or equal to the preset frequency threshold;

The identification module 340 is further configured to identify the user to be identified as a fraudulent user when the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information.

The anti-fraud device provided by the embodiment of the present invention collects the voice signal input by the user to be identified in advance, and obtains the current voiceprint feature parameter by extracting the current voiceprint feature parameter in the current voice signal and matching the pre-saved fraudulent voiceprint feature parameter For the corresponding current frequency value, if the current frequency value is greater than the preset frequency threshold, it is determined that the user to be identified is a fraudulent user, which solves the problem of easy misjudgment when identifying fraudulent behavior in the prior art, and improves the accuracy of fraudulent behavior identification , To further protect the user's property security.

Embodiment Four

FIG. 4 is a schematic structural diagram of a server in Embodiment 4 of the present invention. Figure 4 shows a block diagram of an exemplary server 412 suitable for use in implementing embodiments of the present invention. The server 412 shown in FIG. 4 is only an example, and should not limit the functions and scope of use of this embodiment of the present invention.

As shown in FIG. 4, server 412 takes the form of a general-purpose computing device. Components of server 412 may include, but are not limited to: one or more processors or processing units 416, system memory 428, bus 418 connecting various system components including system memory 428 and processing unit 416.

Bus 418 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. These architectures include, by way of example, but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.

Server 412 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by server 412 and include both volatile and nonvolatile media, removable and non-removable media.

System memory 428 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 430 and/or cache memory 432 . Server 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a disk drive for reading and writing to removable nonvolatile disks (e.g., "floppy disks") may be provided, as well as for removable nonvolatile optical disks (e.g., CD-ROM, DVD-ROM). or other optical media) CD-ROM drive. In these cases, each drive may be connected to bus 418 through one or more data media interfaces. Memory 428 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present invention.

A program/utility 440 having a set (at least one) of program modules 442, such as but not limited to, an operating system, one or more application programs, other program modules, and program data, may be stored, for example, in memory 428 , each or some combination of these examples may include implementations of network environments. Program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.

Server 412 may also communicate with one or more external devices 414 (e.g., keyboards, pointing devices, displays 424, etc.), and may also communicate with one or more devices that enable a user to interact with the server 412, and/or communicate with the Server 412 is capable of communicating with any device (eg, network card, modem, etc.) that communicates with one or more other computing devices. Such communication may be performed through an input/output (I/O) interface 422, wherein the input interface mainly receives the current user's voice signal. Moreover, the server 412 can also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN) and/or a public network, such as the Internet) through a network adapter 420 . As shown, network adapter 420 communicates with other modules of server 412 over bus 418 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with server 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and Data backup storage system, etc.

The processing unit 416 executes various functional applications and data processing by running the program stored in the system memory 428, such as implementing the anti-fraud identification method provided by the embodiment of the present invention, including:

When receiving the voice collection instruction sent by the user to be identified, collect the current voice signal input by the user to be identified according to the preset input method;

Extracting the current voiceprint feature parameters in the current voice signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters or vocal cord feature parameters;

When the current voiceprint feature parameter matches the pre-saved fraudulent voiceprint feature parameter, the current frequency value corresponding to the current voiceprint feature parameter is obtained in the database;

When the current frequency value is greater than the preset frequency threshold, the user to be identified is identified as a fraudulent user.

Embodiment five

Embodiment 5 of the present invention provides a storage medium on which a computer program is stored. When the program is executed by a processor, the anti-fraud identification method provided in the embodiment of the present invention is implemented, including:

When receiving the voice collection instruction sent by the user to be identified, collect the current voice signal input by the user to be identified according to the preset input method;

Extracting the current voiceprint feature parameters in the current voice signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters or vocal cord feature parameters;

When the current voiceprint feature parameter matches the pre-saved fraudulent voiceprint feature parameter, the current frequency value corresponding to the current voiceprint feature parameter is obtained in the database;

When the current frequency value is greater than the preset frequency threshold, the user to be identified is identified as a fraudulent user.

The storage medium provided by the embodiments of the present invention may use any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including - but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out the operations of the present invention may be written in one or more programming languages, or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, and conventional Procedural Programming Language - such as "C" or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (such as through an Internet Service Provider). Internet connection).

Note that the above are only preferred embodiments of the present invention and applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present invention, and the present invention The scope is determined by the scope of the appended claims.

Claims (10)

1. An anti-fraud identification method, characterized in that the method comprises: When receiving the voice collection instruction sent by the user to be identified, collect the current voice signal input by the user to be identified according to the preset input mode; Extracting the current voiceprint feature parameters in the current speech signal; wherein, the current voiceprint feature parameters include: oral cavity feature parameters, nasal sound feature parameters, flat tongue roll feature parameters or vocal cord feature parameters; When the current voiceprint characteristic parameter matches the pre-saved fraudulent voiceprint characteristic parameter, obtain the current frequency value corresponding to the current voiceprint characteristic parameter in the database; When the current frequency value is greater than a preset frequency threshold, the user to be identified is identified as a fraudulent user. 2. The method according to claim 1, wherein the collecting the current voice signal input by the user to be identified according to a preset input mode comprises: randomly generating password information for acquiring the current voice signal, and displaying the password information to the user to be identified; Collecting the current voice signal submitted by the user to be identified after reading the password information. 3. The method according to claim 1, wherein the obtaining the current frequency value corresponding to the current voiceprint feature parameter in the database comprises: Obtaining the user identification of the user to be identified in the voice collection instruction; The current frequency value corresponding to the current voiceprint feature parameter is acquired from the database according to the user identifier. 4. The method according to claim 1, wherein the method further comprises: When the current frequency value is less than or equal to the preset frequency threshold, receiving the basic information input by the user to be identified; When the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information, the user to be identified is identified as a fraudulent user. 5. An anti-fraud identification device, characterized in that the device comprises: a receiving module, an extraction module, an acquisition module and an identification module; wherein, The receiving module is configured to collect a current voice signal input by the user to be identified according to a preset input mode when receiving a voice collection instruction sent by the user to be identified; The extraction module is used to extract the current voiceprint characteristic parameters in the current speech signal; wherein, the current voiceprint characteristic parameters include: oral cavity characteristic parameters, nasal sound characteristic parameters, tongue rolling and flattening characteristic parameters or vocal cord characteristic parameters; The acquisition module is configured to acquire the current frequency value corresponding to the current voiceprint characteristic parameter in the database when the current voiceprint characteristic parameter matches the pre-saved fraudulent voiceprint characteristic parameter; The identification module is configured to identify the user to be identified as a fraudulent user when the current frequency value is greater than a preset frequency threshold. 6. The device according to claim 5, wherein the receiving module is specifically configured to randomly generate password information for acquiring the current voice signal, and display the password information to the user to be identified; Collecting the current voice signal submitted by the user to be identified after reading the password information. 7. The device according to claim 5, wherein the acquiring module is specifically configured to acquire the user identification of the user to be identified in the voice collection instruction; according to the user identification in the database Obtain the current frequency value corresponding to the current voiceprint feature parameter. 8. The device according to claim 5, wherein the receiving module is further configured to receive the basic frequency input by the user to be recognized when the current frequency value is less than or equal to the preset frequency threshold. information; The identification module is further configured to identify the user to be identified as a fraudulent user when the basic information input by the user to be identified is the same as the pre-saved fraudulent basic information. 9. A server, characterized in that the server comprises: one or more processors; storage means for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are made to implement the anti-fraud identification method according to any one of claims 1-4. 10. A computer-readable storage medium, on which a computer program is stored, wherein when the program is executed by a processor, the anti-fraud identification method according to any one of claims 1-4 is implemented.