CN114900712A - Real content analysis system of mobile terminal - Google Patents
Real content analysis system of mobile terminal Download PDFInfo
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- CN114900712A CN114900712A CN202210702348.XA CN202210702348A CN114900712A CN 114900712 A CN114900712 A CN 114900712A CN 202210702348 A CN202210702348 A CN 202210702348A CN 114900712 A CN114900712 A CN 114900712A
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 68
- 230000000306 recurrent effect Effects 0.000 claims abstract description 64
- 230000003796 beauty Effects 0.000 claims abstract description 41
- 230000007246 mechanism Effects 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 7
- 230000009471 action Effects 0.000 claims description 24
- 230000000875 corresponding effect Effects 0.000 claims description 22
- 230000002596 correlated effect Effects 0.000 claims description 5
- 230000003139 buffering effect Effects 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 238000003909 pattern recognition Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000001815 facial effect Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/21—Server components or server architectures
- H04N21/218—Source of audio or video content, e.g. local disk arrays
- H04N21/2187—Live feed
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/4788—Supplemental services, e.g. displaying phone caller identification, shopping application communicating with other users, e.g. chatting
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
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- General Engineering & Computer Science (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The invention relates to a real content analysis system of a mobile terminal, which comprises: the video caching mechanism is arranged in a mobile terminal running a main broadcast live APP and used for receiving a video clip sent from a live broadcast video server and caching the video clip as a current video clip; a content selection mechanism for performing a deduplication process on respective video pictures constituting a current video clip to obtain a plurality of remaining pictures; and the mode identification device is used for inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network to obtain the beauty identification result output by the recurrent neural network. By the method and the device, the effective authentication of the beauty mode based on the neural network can be realized by adopting a customized video frame selection mechanism and a multi-frame joint authentication mode, so that the authenticity of a main picture at a user of the mobile terminal is effectively maintained.
Description
Technical Field
The invention relates to the field of mobile terminals, in particular to a real content analysis system of a mobile terminal.
Background
With the development of computer technology, the mobile terminal enters a human-centered mode from an equipment-centered mode, integrates embedded computing, control technology, artificial intelligence technology, biometric authentication technology and the like, and fully embodies the human-oriented purpose. Due to the development of software technology, the mobile terminal can adjust the setting according to personal requirements, and is more personalized. Meanwhile, the mobile terminal integrates a plurality of software and hardware, and the functions are more and more powerful.
In the prior art, in a mobile terminal running a anchor live broadcast APP, a received video clip is usually a false and special-effect video provided by the anchor live broadcast APP and having a skin-beautifying effect added thereto in a plurality of different skin-beautifying modes, although the entertainment and the aesthetic property of video content can be increased, a user watching the video clips is also deceived to a certain extent, at least some options for removing the skin-beautifying effect should be provided for the user to select when the skin-beautifying mode of the current video clip is identified.
Disclosure of Invention
In order to solve the problems, the invention provides a system for analyzing the real content of a mobile terminal, aiming at the technical problem that the discrimination operation of whether the beauty effect is added or not is difficult to be carried out on false and special-effect videos with the beauty effect added by the mobile terminal where a user is located in the prior art, the customized video frame selection mechanism and the multi-frame joint discrimination mode are adopted to carry out the effective discrimination of the beauty mode based on the neural network, so that objective data and more options are provided for the user of the mobile terminal.
According to an aspect of the present invention, there is provided a real content parsing system for a mobile terminal, the system including:
the video caching mechanism is arranged in a mobile terminal running a main broadcast live APP and used for receiving a video clip sent from a live broadcast video server and caching the video clip as a current video clip;
the content selection mechanism is connected with the video cache mechanism and is used for executing deduplication processing on each video picture forming the current video clip so as to obtain a plurality of residual pictures;
the time-sharing processing mechanism is connected with the content selection mechanism and is used for selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps respectively corresponding to the plurality of residual pictures to be used as a plurality of input contents of the set total number;
the mode identification device is arranged in the mobile terminal, is connected with the time-sharing processing mechanism and is used for inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network to obtain a beautifying identification result output by the recurrent neural network;
network generating means connected to the pattern identifying means for generating a recurrent neural network required by the pattern identifying means;
wherein generating the recurrent neural network required for the pattern recognition device comprises: sending the recurrent neural network after each learning action is executed to the pattern identification device for use by the pattern identification device;
wherein sending the recurrent neural network after each learning action is performed to the pattern identifying device to be used by the pattern identifying device comprises: the number of times of the learning action executed by the recurrent neural network is positively correlated with the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP;
wherein, selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps corresponding to the plurality of residual pictures respectively to be used as a plurality of input contents of the set total number comprises: the value of the set total is in direct proportion to the total number of beauty modes existing in the anchor live broadcast APP.
It can be seen that the present invention has at least three significant technological advances:
firstly, when a mobile terminal running a main broadcast live broadcast APP performs multi-frame picture centralized identification based on a recurrent neural network on whether a main broadcast current video clip uses a beauty mode, so that real information is provided for a user using the mobile terminal; secondly, the structures of the recurrent neural networks established by each anchor live broadcast APP are different, specifically, the times of the executed learning actions of the recurrent neural networks are positively correlated with the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP, and the number of the input ends of the recurrent neural networks is in direct proportion to the total number of the beauty modes existing in the anchor live broadcast APP; thirdly, the main broadcasting current video clip is subjected to de-duplication and time uniform processing to obtain a multi-frame picture used as the input content of the recurrent neural network, so that the objectivity of the reference data for performing identification is improved.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a block diagram illustrating a structure of a real content parsing system of a mobile terminal according to a first embodiment of the present invention.
Fig. 2 is a block diagram illustrating a structure of a real content parsing system of a mobile terminal according to a second embodiment of the present invention.
Fig. 3 is a block diagram illustrating a structure of a real content parsing system of a mobile terminal according to a third embodiment of the present invention.
Detailed Description
An embodiment of the mobile terminal real content parsing system of the present invention will be described in detail below with reference to the accompanying drawings.
According to different installation sources of the mobile phone APP, the mobile phone APP can be divided into mobile terminal pre-installed software and third-party application software installed by a user. The mobile terminal pre-installed software generally refers to applications or software which are pre-installed in a consumer mobile terminal in a way that a mobile terminal leaves a factory and is self-provided or a third-party flashing channel is pre-installed in the consumer mobile terminal and cannot be deleted by a consumer. Besides the pre-installed software of the mobile terminal, the user downloads the installed third-party mobile phone APP from the mobile terminal application market, and the downloading type is mainly concentrated on the social community software. In the prior art, in a mobile terminal running a anchor live broadcast APP, a received video clip is usually a false and special-effect video provided by the anchor live broadcast APP and having a skin-beautifying effect added thereto in a plurality of different skin-beautifying modes, although the entertainment and the aesthetic property of video content can be increased, a user watching the video clips is also deceived to a certain extent, at least some options for removing the skin-beautifying effect should be provided for the user to select when the skin-beautifying mode of the current video clip is identified.
In order to overcome the defects, the invention builds a real content analysis system of the mobile terminal, and can effectively solve the corresponding technical problem.
The invention has at least the following three remarkable technical improvements:
firstly, when a mobile terminal running a main broadcast live broadcast APP performs multi-frame picture centralized identification based on a recurrent neural network on whether a main broadcast current video clip uses a beauty mode, so that real information is provided for a user using the mobile terminal; secondly, the structures of the recurrent neural networks established by each anchor live broadcast APP are different, specifically, the times of the executed learning actions of the recurrent neural networks are positively correlated with the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP, and the number of the input ends of the recurrent neural networks is in direct proportion to the total number of the beauty modes existing in the anchor live broadcast APP; thirdly, the main broadcasting current video clip is subjected to de-duplication and time uniform processing to obtain a multi-frame picture used as the input content of the recurrent neural network, so that the objectivity of the reference data for performing identification is improved.
Fig. 1 is a block diagram showing a structure of a real content parsing system of a mobile terminal according to a first embodiment of the present invention, the system including:
the video caching mechanism is arranged in a mobile terminal running a main broadcast live APP and used for receiving a video clip sent from a live broadcast video server and caching the video clip as a current video clip;
the content selection mechanism is connected with the video cache mechanism and is used for executing duplication elimination processing on each video picture forming the current video clip so as to obtain a plurality of residual pictures;
the time-sharing processing mechanism is connected with the content selection mechanism and is used for selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps respectively corresponding to the plurality of residual pictures to be used as a plurality of input contents of the set total number;
the mode identification device is arranged in the mobile terminal, is connected with the time-sharing processing mechanism and is used for inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network to obtain a beautifying identification result output by the recurrent neural network;
network generating means connected to the pattern identifying means for generating a recurrent neural network required by the pattern identifying means;
wherein generating the recurrent neural network required for the pattern recognition device comprises: sending the recurrent neural network after each learning action is executed to the pattern identification device for use by the pattern identification device;
wherein sending the recurrent neural network after each learning action is performed to the pattern identifying device to be used by the pattern identifying device comprises: the number of times of the learning action executed by the recurrent neural network is positively correlated with the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP;
for example, when the more types of beauty special effects are integrated in a certain beauty mode, the higher the algorithm complexity is;
in addition, the complexity of the algorithm can be determined according to the operation amount executed by a certain beautifying mode, and the more the operation amount executed by a certain beautifying mode is, the higher the complexity of the determined algorithm is;
wherein, selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps corresponding to the plurality of residual pictures respectively to be used as a plurality of input contents of the set total number comprises: the value of the set total is in direct proportion to the total number of beauty modes existing in the anchor live broadcast APP.
Next, a detailed description will be given of a specific configuration of the mobile terminal real content analysis system according to the present invention.
Fig. 2 is a block diagram illustrating a structure of a real content parsing system of a mobile terminal according to a second embodiment of the present invention.
Compared to the first embodiment of the present invention, the real content parsing system of the mobile terminal in fig. 2 may further include:
and the auxiliary display device is arranged in the mobile terminal, is connected with the mode identification device and the video cache mechanism respectively, and is used for displaying the beauty identification result output by the mode identification device while displaying the current video clip on a display screen of the mobile terminal in real time by the anchor live broadcast APP.
Fig. 3 is a block diagram illustrating a structure of a real content parsing system of a mobile terminal according to a third embodiment of the present invention.
Compared to the second embodiment of the present invention, the real content parsing system of the mobile terminal in fig. 3 may further include:
the time sequence control device is arranged in the mobile terminal, is respectively connected with the auxiliary display device, the display screen and the video cache mechanism, and is used for realizing synchronous action of the auxiliary display device, the display screen and the video cache mechanism;
wherein implementing the synchronized actions of the auxiliary display device and the video caching mechanism comprises: the display action of the auxiliary display device and the display screen occurs after the buffering action of the video buffering mechanism.
In the real content analysis system of the mobile terminal according to any embodiment of the invention:
the mean forward correlation of the number of times the recurrent neural network is performed with the algorithm complexity of the various beauty modes present in the anchor live APP includes: the smaller the numerical value of the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP is, the fewer the times of the learning action executed by the recurrent neural network is;
wherein, the value of setting for the total number and the total number of the beauty mode that live APP exists of anchor directly broadcast directly proportional include: the smaller the total number of beauty modes existing in the anchor live broadcast APP is, the smaller the value of the determined set total number is.
In the real content analysis system of the mobile terminal according to any embodiment of the invention:
sending the recurrent neural network after each learning action is performed to the pattern identifying device to be used by the pattern identifying device includes: executing each learning action by adopting a section of video segment with a facial beautification identification result, wherein a plurality of input contents of a set total number corresponding to the section of video are input to the input end of the recurrent neural network, and the facial beautification identification result corresponding to the section of video is used as the output content of the output end of the recurrent neural network;
the method for obtaining the beauty appraisal result output by the recurrent neural network by inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network comprises the following steps: when the cyclic neural network outputs a coding numerical value corresponding to FALSE, judging that the anchor in the current video clip uses a beauty mode;
the method for obtaining the beauty appraisal result output by the recurrent neural network by inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network comprises the following steps: and when the recurrent neural network outputs the coding numerical value corresponding to the TURE, judging that the anchor in the current video clip uses the plain color mode.
In the real content analysis system of the mobile terminal according to any embodiment of the invention:
selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of time stamps respectively corresponding to the plurality of residual pictures to be used as a plurality of input contents of the set total number, wherein the selection comprises the following steps: the time intervals of every two timestamps corresponding to the rest pictures with the set total number of the uniform time intervals are equal.
In the real content analysis system of the mobile terminal according to any embodiment of the invention:
receiving a video clip sent from a live video server and caching the video clip as a current video clip comprises: the live video server is a big data application network element;
the method for receiving the video clip sent from the live video server and caching the video clip as the current video clip comprises the following steps: and caching the video clips by adopting a first-in first-out storage mode.
In addition, in the real content analysis system of the mobile terminal, each learning action is executed by using a segment of video with a facial beautification identification result, wherein a plurality of input contents of a set total number corresponding to the segment of video are input to an input end of the recurrent neural network, and the taking of the facial beautification identification result corresponding to the segment of video as an output content of an output end of the recurrent neural network includes: the input contents of the set total number corresponding to the video segment are obtained by continuously processing the video segment through the video caching mechanism, the content selection mechanism and the time-sharing processing mechanism.
By adopting the real content analysis system of the mobile terminal, aiming at the technical problems that the mobile terminal user is often falsely and masked by the beauty video picture in the prior art, the effective authentication of the beauty mode based on the neural network is realized by adopting the customized video frame selection mechanism and the multi-frame joint authentication mode, so that the authenticity of the anchor picture of the user of the mobile terminal is effectively maintained.
As described herein, one of ordinary skill will recognize that numerous variations and modifications may be made without departing from the present invention in its broader aspects.
Claims (10)
1. A real content parsing system of a mobile terminal, the system comprising:
the video caching mechanism is arranged in a mobile terminal running a main broadcast live APP and used for receiving a video clip sent from a live broadcast video server and caching the video clip as a current video clip;
the content selection mechanism is connected with the video cache mechanism and is used for executing duplication elimination processing on each video picture forming the current video clip so as to obtain a plurality of residual pictures;
the time-sharing processing mechanism is connected with the content selection mechanism and is used for selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps respectively corresponding to the plurality of residual pictures to be used as a plurality of input contents of the set total number;
the mode identification device is arranged in the mobile terminal, is connected with the time-sharing processing mechanism and is used for inputting a plurality of input contents with set total number into the input end of the recurrent neural network and operating the recurrent neural network to obtain a beautifying identification result output by the recurrent neural network;
network generating means connected to the pattern identifying means for generating a recurrent neural network required by the pattern identifying means;
wherein generating the recurrent neural network required for the pattern recognition device comprises: sending the recurrent neural network after each learning action is executed to the pattern identification device for use by the pattern identification device;
wherein sending the recurrent neural network after each learning action is performed to the pattern identifying device to be used by the pattern identifying device comprises: the number of times of the learning action executed by the recurrent neural network is positively correlated with the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP;
wherein, selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of timestamps corresponding to the plurality of residual pictures respectively to be used as a plurality of input contents of the set total number comprises: the value of the set total is in direct proportion to the total number of beauty modes existing in the anchor live broadcast APP.
2. The system for parsing real content of a mobile terminal according to claim 1, wherein the system further comprises:
and the auxiliary display device is arranged in the mobile terminal, is connected with the mode identification device and the video cache mechanism respectively, and is used for displaying the beauty identification result output by the mode identification device while displaying the current video clip on a display screen of the mobile terminal in real time by the anchor live broadcast APP.
3. The system for parsing real content of a mobile terminal according to claim 2, wherein said system further comprises:
and the time sequence control device is arranged in the mobile terminal, is respectively connected with the auxiliary display device, the display screen and the video cache mechanism, and is used for realizing the synchronous action of the auxiliary display device, the display screen and the video cache mechanism.
4. The real content parsing system of mobile terminal according to claim 3, wherein:
the method for realizing the synchronous action of the auxiliary display device and the video caching mechanism comprises the following steps: the display action of the auxiliary display device and the display screen occurs after the buffering action of the video buffering mechanism.
5. The system for parsing real content of a mobile terminal according to any one of claims 1 to 4, wherein:
the mean forward correlation of the number of times the recurrent neural network is performed with the algorithm complexity of the various beauty modes present in the anchor live APP includes: the smaller the numerical value of the mean value of the algorithm complexity of various beauty modes existing in the anchor live broadcast APP is, the fewer the times of the learning action executed by the recurrent neural network is;
wherein, the value of setting for the total number and the total number of the beauty mode that live APP exists of anchor directly broadcast directly proportional include: the smaller the total number of beauty modes existing in the anchor live broadcast APP is, the smaller the value of the determined set total number is.
6. The system for parsing real content of a mobile terminal according to any one of claims 1 to 4, wherein:
sending the recurrent neural network after each learning action is performed to the pattern identifying device to be used by the pattern identifying device includes: and executing each learning action by adopting a section of video segment with the existing beauty identification result, wherein a plurality of input contents of the set total number corresponding to the section of video are input to the input end of the recurrent neural network, and the beauty identification result corresponding to the section of video is used as the output content of the output end of the recurrent neural network.
7. The real content parsing system of mobile terminal according to claim 6, wherein:
inputting a set total number of input contents into an input end of a recurrent neural network and operating the recurrent neural network to obtain a beauty appraisal result output by the recurrent neural network, wherein the beauty appraisal result comprises the following steps: and when the recurrent neural network outputs the coding numerical value corresponding to FALSE, judging that the anchor in the current video segment uses a beauty mode.
8. The system for parsing real content of a mobile terminal according to claim 7, wherein:
inputting a set total number of input contents into an input end of a recurrent neural network and operating the recurrent neural network to obtain a beauty appraisal result output by the recurrent neural network, wherein the beauty appraisal result comprises the following steps: and when the recurrent neural network outputs the coding numerical value corresponding to the TURE, judging that the anchor in the current video clip uses the plain color mode.
9. The system for parsing real content of a mobile terminal according to any one of claims 1 to 4, wherein:
selecting a plurality of residual pictures with uniform time intervals of a set total number according to a plurality of time stamps respectively corresponding to the plurality of residual pictures to be used as a plurality of input contents of the set total number, wherein the selection comprises the following steps: the time intervals of every two timestamps corresponding to the rest pictures with the set total number of the uniform time intervals are equal.
10. The system for parsing real content of a mobile terminal according to any one of claims 1 to 4, wherein:
receiving a video clip sent from a live video server and caching the video clip as a current video clip comprises: the live video server is a big data application network element;
the method for receiving the video clip sent from the live video server and caching the video clip as the current video clip comprises the following steps: and caching the video clips by adopting a first-in first-out storage mode.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210702348.XA CN114900712A (en) | 2022-06-21 | 2022-06-21 | Real content analysis system of mobile terminal |
| PCT/CN2022/113166 WO2023245846A1 (en) | 2022-06-21 | 2022-08-18 | Mobile terminal real content analysis system, built-in anti-fraud and fraud judgment system and method |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210702348.XA CN114900712A (en) | 2022-06-21 | 2022-06-21 | Real content analysis system of mobile terminal |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115147312A (en) * | 2022-08-10 | 2022-10-04 | 田海艳 | Facial skin-grinding special effect simplified identification platform |
| WO2023245846A1 (en) * | 2022-06-21 | 2023-12-28 | 喻荣先 | Mobile terminal real content analysis system, built-in anti-fraud and fraud judgment system and method |
-
2022
- 2022-06-21 CN CN202210702348.XA patent/CN114900712A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023245846A1 (en) * | 2022-06-21 | 2023-12-28 | 喻荣先 | Mobile terminal real content analysis system, built-in anti-fraud and fraud judgment system and method |
| CN115147312A (en) * | 2022-08-10 | 2022-10-04 | 田海艳 | Facial skin-grinding special effect simplified identification platform |
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