CN114025224A - Network video traceability system based on deep learning - Google Patents
Network video traceability system based on deep learning Download PDFInfo
- Publication number
- CN114025224A CN114025224A CN202111372350.7A CN202111372350A CN114025224A CN 114025224 A CN114025224 A CN 114025224A CN 202111372350 A CN202111372350 A CN 202111372350A CN 114025224 A CN114025224 A CN 114025224A
- Authority
- CN
- China
- Prior art keywords
- unit
- network video
- video
- network
- tracing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013135 deep learning Methods 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 37
- 238000004891 communication Methods 0.000 claims abstract description 13
- 238000013528 artificial neural network Methods 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 6
- 230000009471 action Effects 0.000 claims description 17
- 238000000605 extraction Methods 0.000 claims description 17
- 238000007781 pre-processing Methods 0.000 claims description 11
- 239000000284 extract Substances 0.000 claims description 10
- 238000004148 unit process Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000001788 irregular Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000012550 audit Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- H04N21/433—Content storage operation, e.g. storage operation in response to a pause request, caching operations
- H04N21/4334—Recording operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- 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
- H04N21/2183—Cache memory
-
- 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, manipulating MPEG-4 scene graphs
- H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
-
- 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
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/254—Management at additional data server, e.g. shopping server, rights management server
- H04N21/2541—Rights Management
-
- 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
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25808—Management of client data
- H04N21/25816—Management of client data involving client authentication
-
- 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
- H04N21/431—Generation of visual interfaces for content selection or interaction; Content or additional data rendering
- H04N21/4312—Generation of visual interfaces for content selection or interaction; Content or additional data rendering involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations
-
- 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, rendering scenes according to MPEG-4 scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
-
- 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
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/454—Content or additional data filtering, e.g. blocking advertisements
-
- 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
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/462—Content or additional data management, e.g. creating a master electronic program guide from data received from the Internet and a Head-end, controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabilities
- H04N21/4627—Rights management associated to the content
Abstract
The invention discloses a network video traceability system based on deep learning, which comprises a publisher user side, a cloud server, a user client side, an artificial neural network, a video processing module and a traceability module, wherein the publisher user side is used for publishing a network video by a publisher, the cloud server is used for receiving, processing, storing and sending the network video, the user client side is used for receiving the network video by a user, and the artificial neural network is used for establishing network communication among the user side, the cloud server and the user client side; the method and the device have the advantages that the traceability recording and traceability of the network video can be realized while the traceability of the video author is traced, the network video author can know the traceability and the downloading of the video works conveniently, the use effect is better, the preliminary audit of the network video data is realized, the illegal video is prevented from being uploaded, reloaded and downloaded, the labor of manual audit is reduced, and the method and the device are more practical.
Description
Technical Field
The invention relates to the field of network video processing, in particular to a network video traceability system based on deep learning.
Background
With the development of science and technology, network communication coverage is more and more extensive, videos become a main tool for people to record information, various video works are streamed on a network, each network video is manufactured by an author, and a user can carry out transshipment or download at the same time, and the network video traceability system for deep learning is a network video traceability system based on an artificial neural network and is mainly used for tracing video authors;
firstly, the existing deep learning network video traceability system cannot trace the source of a transshipped and downloaded user end, so that an author cannot know the transshipped and downloaded traceability of the manufactured network video, and the use effect is poor; secondly, the existing network video traceability system for deep learning cannot preliminarily check the video and cannot intercept the illegal video, and the functionality is poor.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing deep learning network video traceability system cannot trace the source of the transshipped and downloaded user end, so that an author cannot know the source of the manufactured network video transshipped and downloaded, and the use effect is poor; secondly, the existing network video traceability system for deep learning cannot preliminarily check the video, cannot intercept the illegal video, and is poor in functionality.
The invention solves the technical problems through the following technical scheme, and the network video traceability system based on deep learning comprises a publisher user side, a cloud server, a user client side, an artificial neural network, a video processing module and a traceability module;
the publisher user side is used for a publisher to publish the network video;
the cloud server is used for receiving, processing, storing and sending network videos;
the user client is used for receiving the network video by the user;
the artificial neural network is used for establishing network communication among a user side, a cloud server and a user client side;
the video processing module is used for processing video data;
the source tracing module is used for source tracing.
Preferably, the video processing module includes a receiving unit, a preprocessing unit, an auditing unit, an identifying library, a processing unit and a transmitting unit.
Preferably, the video processing module specifically comprises the following processing steps:
the method comprises the following steps: the receiving unit receives the network video uploaded by the publisher user side and transmits the network video to the preprocessing unit;
step two: the preprocessing unit compresses and integrates the network video data;
step three: the preprocessed video data are transmitted to an auditing unit for auditing, and the identification unit identifies the characteristic information in the network video data after the auditing is passed;
step four: the identification unit identifies the identified characteristic information as a source tracing root of the network video and stores the source tracing root into an identification library;
step five: the processing unit processes the network video and then transmits the processed network video to the cloud server for storage.
Preferably, the auditing unit includes a feature extraction unit, a flash memory unit, a comparison unit, a feature library, an interception unit and an output unit, and the auditing unit specifically includes the following processing steps:
step 1: the feature extraction unit extracts the network video data features, and the extracted features are stored in the flash memory unit;
step 2: the comparison unit compares the extracted features with the data in the feature library, and the video data successfully compared are intercepted by the interception unit;
and step 3: the network video data which is not successfully compared is output to the identification unit by the output unit.
Preferably, the comparison formula of the comparison unit is as follows:
the characteristic library data is A { };
the extracted characteristic data is B { };
wherein
Adding new terms;
wherein
Is a delete item;
wherein B ≧ A ═ { x | x ∈ B and x ∈ A } common term;
the common items represent successful comparison, and the extracted feature data has an irregular phenomenon.
Preferably, the tracing module includes an action recognition unit, an extraction unit, a storage unit, a feedback unit, a matching unit and a communication unit, and the tracing module specifically processes the following steps:
step (1): when a user client carries out the transshipment and downloading actions on the network video, the action identification unit identifies and records the action;
step (2): the extraction unit extracts the characteristic information of the user client and the network video information during the transshipment or the downloading, identifies the characteristic information of the user client as a transshipment tracing root or a downloading tracing root, and finally stores the characteristic information by the storage unit;
and (3): the feedback unit feeds the tracing root and the network video data back to the cloud server, the matching unit is used for matching the network video data of the cloud server, and after the matching is successful, the communication unit feeds the reprinting tracing root or the downloading tracing root of the user client identification back to the publisher client.
Compared with the prior art, the invention has the following advantages:
by arranging the traceability module, when a user client reloads and downloads a network video, the action recognition unit recognizes and records the action, the extraction unit extracts characteristic information of the user client and the network video information during reloading or downloading, the characteristic information of the user client is identified as a reloading traceability root or a downloading traceability root and is finally stored by the storage unit, the feedback unit feeds back the traceability root and the network video data to the cloud server and matches the network video data of the cloud server through the matching unit, and after the matching is successful, the communication unit feeds back the reloading traceability root or the downloading traceability root identified by the user client to the publisher client, so that the network video can be reloaded and downloaded by a user and tracked and traced while tracing the source of a video author, and the network video creation can know the reloading and downloading user of a video work conveniently, The source tracing is carried out, and the using effect is better;
through setting up the module of examining and verifying, the characteristic extraction unit extracts the network video data characteristic, the characteristic of extraction has the flash memory unit to store, compare the unit and carry out the characteristic with the data in the characteristic storehouse with the characteristic and compare successful video data by the interception unit interception, compare unsuccessful network video data and export to the recognition cell by the output unit, realized the preliminary examination and verification to network video data, avoid the video of violating the regulations to be uploaded, reprint, download, also reduce the efforts of artifical examination and verification simultaneously, it is more practical.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a system diagram of a video processing module of the present invention;
FIG. 3 is a system diagram of an audit unit of the present invention;
FIG. 4 is a system diagram of a traceability module of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1 to 4, the present embodiment provides a technical solution: the network video traceability system based on deep learning comprises a publisher client, a cloud server, a user client, an artificial neural network, a video processing module and a traceability module;
the publisher user side is used for the publisher to publish the network video;
the cloud server is used for receiving, processing, storing and sending the network video;
the user client is used for receiving the network video by the user;
the artificial neural network is used for establishing network communication among the user side, the cloud server and the user client side;
the video processing module is used for processing the video data;
the source tracing module is used for source tracing.
The video processing module comprises a receiving unit, a preprocessing unit, an auditing unit, a recognition unit, an identification library, a processing unit and a transmission unit.
The video processing module comprises the following specific processing steps:
the method comprises the following steps: the receiving unit receives the network video uploaded by the publisher user side and transmits the network video to the preprocessing unit;
step two: the preprocessing unit compresses and integrates the network video data;
step three: the preprocessed video data are transmitted to an auditing unit for auditing, and the identification unit identifies the characteristic information in the network video data after the auditing is passed;
step four: the identification unit identifies the identified characteristic information as a source tracing root of the network video and stores the source tracing root into an identification library;
step five: the processing unit processes the network video and then transmits the processed network video to the cloud server for storage.
The auditing unit comprises a feature extraction unit, a flash memory unit, a comparison unit, a feature library, an interception unit and an output unit, and the auditing unit comprises the following specific processing steps:
step 1: the feature extraction unit extracts the network video data features, and the extracted features are stored in the flash memory unit;
step 2: the comparison unit compares the extracted features with the data in the feature library, and the video data successfully compared are intercepted by the interception unit;
and step 3: the network video data which is not successfully compared is output to the identification unit by the output unit.
The comparison formula of the comparison unit is as follows:
the characteristic library data is A { };
the extracted characteristic data is B { };
wherein
Adding new terms;
wherein
Is a delete item;
wherein B ≧ A ═ { x | x ∈ B and x ∈ A } common term;
the common items represent successful comparison, and the extracted feature data has an irregular phenomenon.
The source tracing module comprises an action recognition unit, an extraction unit, a storage unit, a feedback unit, a matching unit and a communication unit, and the source tracing module specifically comprises the following processing steps:
step (1): when a user client carries out the transshipment and downloading actions on the network video, the action identification unit identifies and records the action;
step (2): the extraction unit extracts the characteristic information of the user client and the network video information during the transshipment or the downloading, identifies the characteristic information of the user client as a transshipment tracing root or a downloading tracing root, and finally stores the characteristic information by the storage unit;
and (3): the feedback unit feeds the tracing root and the network video data back to the cloud server, the matching unit is used for matching the network video data of the cloud server, and after the matching is successful, the communication unit feeds the reprinting tracing root or the downloading tracing root of the user client identification back to the publisher client.
In summary, when the invention is used, firstly, the receiving unit receives the network video uploaded by the publisher user end and transmits the network video to the preprocessing unit, the preprocessing unit compresses and integrates the network video data, the preprocessed video data is transmitted to the auditing unit for auditing, the feature extraction unit extracts the features of the network video data, the extracted features are stored in the flash memory unit, the comparison unit compares the extracted features with the data in the feature library, the video data successfully compared is intercepted by the interception unit, the network video data unsuccessfully compared is output to the identification unit by the output unit, the identification unit identifies the feature information in the network video data, the identification unit identifies the identified feature information as the tracing root of the network video and stores the tracing root in the identification library, the processing unit processes the network video and transmits the processed network video to the cloud server by the transmission unit for storage, when a user client carries out transferring and downloading actions on a network video, the action recognition unit recognizes and records the actions, the extraction unit extracts characteristic information of the user client and the network video information during transferring or downloading, the characteristic information of the user client is identified as a transferring traceability root or a downloading traceability root, the characteristic information is finally stored by the storage unit, the feedback unit feeds back the traceability root and the network video data to the cloud server and matches the network video data of the cloud server through the matching unit, and after the matching is successful, the communication unit feeds back the transferring traceability root or the downloading traceability root identified by the user client to the publisher client.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. The network video traceability system based on deep learning is characterized by comprising a publisher client, a cloud server, a user client, an artificial neural network, a video processing module and a traceability module;
the publisher user side is used for a publisher to publish the network video;
the cloud server is used for receiving, processing, storing and sending network videos;
the user client is used for receiving the network video by the user;
the artificial neural network is used for establishing network communication among a user side, a cloud server and a user client side;
the video processing module is used for processing video data;
the source tracing module is used for source tracing.
2. The deep learning based network video traceability system of claim 1, wherein: the video processing module comprises a receiving unit, a preprocessing unit, an auditing unit, a recognition unit, an identification library, a processing unit and a transmission unit.
3. The deep learning based network video traceability system of claim 2, wherein: the video processing module comprises the following specific processing steps:
the method comprises the following steps: the receiving unit receives the network video uploaded by the publisher user side and transmits the network video to the preprocessing unit;
step two: the preprocessing unit compresses and integrates the network video data;
step three: the preprocessed video data are transmitted to an auditing unit for auditing, and the identification unit identifies the characteristic information in the network video data after the auditing is passed;
step four: the identification unit identifies the identified characteristic information as a source tracing root of the network video and stores the source tracing root into an identification library;
step five: the processing unit processes the network video and then transmits the processed network video to the cloud server for storage.
4. The deep learning based network video traceability system of claim 2, wherein: the auditing unit comprises a feature extraction unit, a flash memory unit, a comparison unit, a feature library, an interception unit and an output unit, and the auditing unit comprises the following specific processing steps:
step 1: the feature extraction unit extracts the network video data features, and the extracted features are stored in the flash memory unit;
step 2: the comparison unit compares the extracted features with the data in the feature library, and the video data successfully compared are intercepted by the interception unit;
and step 3: the network video data which is not successfully compared is output to the identification unit by the output unit.
5. The deep learning based network video traceability system of claim 4, wherein: the comparison formula of the comparison unit is as follows:
the characteristic library data is A { };
the extracted characteristic data is B { };
wherein
Adding new terms;
wherein
Is a delete item;
wherein B ≧ A ═ { x | x ∈ B and x ∈ A } common term;
the common items represent successful comparison, and the extracted feature data has an irregular phenomenon.
6. The deep learning based network video traceability system of claim 1, wherein: the tracing module comprises an action recognition unit, an extraction unit, a storage unit, a feedback unit, a matching unit and a communication unit, and the tracing module specifically comprises the following processing steps:
step (1): when a user client carries out the transshipment and downloading actions on the network video, the action identification unit identifies and records the action;
step (2): the extraction unit extracts the characteristic information of the user client and the network video information during the transshipment or the downloading, identifies the characteristic information of the user client as a transshipment tracing root or a downloading tracing root, and finally stores the characteristic information by the storage unit;
and (3): the feedback unit feeds the tracing root and the network video data back to the cloud server, the matching unit is used for matching the network video data of the cloud server, and after the matching is successful, the communication unit feeds the reprinting tracing root or the downloading tracing root of the user client identification back to the publisher client.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111372350.7A CN114025224A (en) | 2021-11-18 | 2021-11-18 | Network video traceability system based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111372350.7A CN114025224A (en) | 2021-11-18 | 2021-11-18 | Network video traceability system based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114025224A true CN114025224A (en) | 2022-02-08 |
Family
ID=80065086
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111372350.7A Pending CN114025224A (en) | 2021-11-18 | 2021-11-18 | Network video traceability system based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114025224A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114567798A (en) * | 2022-02-28 | 2022-05-31 | 南京烽火星空通信发展有限公司 | Tracing method for Internet short video variety |
| CN115297360A (en) * | 2022-09-14 | 2022-11-04 | 百鸣(北京)信息技术有限公司 | Intelligent auditing system for multimedia software video uploading |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105721883A (en) * | 2014-12-05 | 2016-06-29 | 华中科技大学 | Video sharing method and system in cloud storage system based on source tracing information |
| CN109660828A (en) * | 2018-09-27 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Video resource management method, equipment and computer readable storage medium |
| CN109660823A (en) * | 2018-12-28 | 2019-04-19 | 广州华多网络科技有限公司 | Video distribution method, apparatus, electronic equipment and storage medium |
| CN112035701A (en) * | 2020-08-11 | 2020-12-04 | 南京烽火星空通信发展有限公司 | Internet short video source tracing method and system |
| US20210037071A1 (en) * | 2019-07-29 | 2021-02-04 | Steven Thomas Schoenwald | Efficient distribution and display of media |
| CN113613073A (en) * | 2021-08-04 | 2021-11-05 | 北京林业大学 | End-to-end video digital watermarking system and method |
-
2021
- 2021-11-18 CN CN202111372350.7A patent/CN114025224A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105721883A (en) * | 2014-12-05 | 2016-06-29 | 华中科技大学 | Video sharing method and system in cloud storage system based on source tracing information |
| CN109660828A (en) * | 2018-09-27 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Video resource management method, equipment and computer readable storage medium |
| CN109660823A (en) * | 2018-12-28 | 2019-04-19 | 广州华多网络科技有限公司 | Video distribution method, apparatus, electronic equipment and storage medium |
| US20210037071A1 (en) * | 2019-07-29 | 2021-02-04 | Steven Thomas Schoenwald | Efficient distribution and display of media |
| CN112035701A (en) * | 2020-08-11 | 2020-12-04 | 南京烽火星空通信发展有限公司 | Internet short video source tracing method and system |
| CN113613073A (en) * | 2021-08-04 | 2021-11-05 | 北京林业大学 | End-to-end video digital watermarking system and method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114567798A (en) * | 2022-02-28 | 2022-05-31 | 南京烽火星空通信发展有限公司 | Tracing method for Internet short video variety |
| CN114567798B (en) * | 2022-02-28 | 2023-12-12 | 南京烽火星空通信发展有限公司 | Tracing method for short video variety of Internet |
| CN115297360A (en) * | 2022-09-14 | 2022-11-04 | 百鸣(北京)信息技术有限公司 | Intelligent auditing system for multimedia software video uploading |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114025224A (en) | Network video traceability system based on deep learning | |
| CN109325041B (en) | Service data processing method, device, computer equipment and storage medium | |
| CN105786998A (en) | Database middleware system and method for processing data through database middleware system | |
| CN104978351A (en) | Backup method of mass small files and cloud store gateway | |
| CN105490841A (en) | Terminal log grasping method, device and system | |
| CN107291863B (en) | A kind of quick check method and device for industrial control equipment information security | |
| CN103150584A (en) | Communication resource motion processing method and system | |
| CN105791246A (en) | Information verification method, device and system | |
| CN109729044A (en) | A kind of general internet data acquisition is counter to climb system and method | |
| CN111882059A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN106790136A (en) | A kind of identity identifying method based on cloud platform, device and system | |
| CN112330309A (en) | Administrative service data exchange system and method based on block chain | |
| CN105183384A (en) | Direct erasure correction implementation method and device | |
| CN103810417A (en) | Method for releasing alerting signal on the basis of unconformity of host identify and authentication terminal thereof | |
| CN108513034A (en) | Method, electronic device and the computer readable storage medium of long-range shooting picture | |
| CN115563069B (en) | Data sharing processing method and system based on artificial intelligence and cloud platform | |
| CN114817754B (en) | VR learning system | |
| CN110288494A (en) | A kind of trade-mark administration automated system | |
| CN115001807A (en) | User login processing method and device of application program | |
| CN110895563A (en) | Text retrieval method and device | |
| CN112800127A (en) | Data mining analysis method and device based on transaction bill | |
| CN107608708B (en) | Interface data counterfeiting method | |
| CN110598531A (en) | Method and system for recognizing electronic seal based on face of mobile terminal | |
| CN110413691A (en) | Database backup method, restoration methods and device based on block chain | |
| CN110895556B (en) | Text retrieval method and device, storage medium and electronic device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |