CN113763211A - Infringement detection method and device based on block chain and electronic equipment - Google Patents
Infringement detection method and device based on block chain and electronic equipment Download PDFInfo
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Abstract
The embodiment of the specification provides an infringement detection method and device based on a block chain and electronic equipment. The method comprises the following steps: carrying out significance detection on an image to be detected to obtain at least one local sub-image; extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features; carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
Description
Technical Field
The embodiment of the specification relates to the technical field of block chains, in particular to an infringement detection method and device based on the block chains and electronic equipment.
Background
With the increasing awareness of copyright, how to more accurately perform infringement detection is becoming a hotspot.
In the infringement detection for the image, the detection is usually performed based on the global features of the whole image. However, for images with local infringement, such as image stitching and picture-in-picture, global infringement detection cannot be well identified.
Therefore, it is desirable to provide a scheme that can identify local image infringement.
Disclosure of Invention
An embodiment of the present specification provides an infringement detection method and apparatus based on a block chain, and an electronic device:
according to a first aspect of embodiments herein, there is provided a block chain-based infringement detection method, the method including:
carrying out significance detection on an image to be detected to obtain at least one local sub-image;
extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
According to a second aspect of embodiments herein, there is provided a block chain-based infringement detection method, the method including:
receiving an image to be detected uploaded by a client;
carrying out significance detection on an image to be detected to obtain at least one local sub-image;
extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
and when the infringement detection result is that the infringement is not infringed, the image to be detected is stored and verified to a block chain.
According to a third aspect of embodiments herein, there is provided an infringement detection apparatus based on a block chain, the apparatus including:
the saliency detection unit is used for carrying out saliency detection on the image to be detected to obtain at least one local sub-image;
the characteristic extraction unit is used for extracting local characteristics from the at least one local sub-image and constructing a local characteristic vector based on the local characteristics;
the infringement detection unit is used for carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain so as to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
According to a fourth aspect of embodiments herein, there is provided an infringement detection apparatus based on a block chain, the apparatus including:
the image receiving unit is used for receiving the image to be detected uploaded by the client;
the saliency detection unit is used for carrying out saliency detection on the image to be detected to obtain at least one local sub-image;
the characteristic extraction unit is used for extracting local characteristics from the at least one local sub-image and constructing a local characteristic vector based on the local characteristics;
the infringement detection unit is used for carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
and the image evidence storing unit is used for storing the image to be detected into the block chain when the infringement detection result is that the infringement is not infringed.
According to a fifth aspect of embodiments herein, there is provided an electronic apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform any of the above block chain based infringement detection methods.
The embodiment of the specification provides an infringement detection scheme based on a block chain, wherein infringement detection is refined to a local area, and if a local feature vector of an image to be detected is similar to a local feature vector of an original image which is proved to exist in the block chain, the infringement area which infringes the original image exists in the image to be detected. In this manner, locally infringing images, such as picture-in-picture, image stitching, and the like, may be identified.
On the other hand, as the data stored in the block chain has the characteristic of being not falsifiable, a credible local characteristic library can be constructed after the local characteristic vectors extracted from the local sub-images of the original image are stored in the block chain; therefore, the infringement detection result of the infringement detection of the image to be detected based on the local feature library stored on the block chain is also credible; and the uplink of the infringement detection result can be prevented from being tampered, so that the security of the infringement detection result is ensured.
Drawings
Fig. 1 is a schematic diagram of a network environment related to a block chain according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for detecting an infringement based on a conventional blockchain according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for detecting an infringement based on a conventional blockchain according to an embodiment of the present disclosure;
fig. 4 is a hardware structure diagram of an intrusion detection apparatus based on a block chain according to an embodiment of the present specification;
fig. 5 is a block chain-based infringement detection apparatus provided in an embodiment of the present specification;
fig. 6 is a block chain-based infringement detection apparatus provided in an embodiment of the present specification.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the specification, as detailed in the appended claims.
The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although terms, second, third, etc. may be used herein to describe various information, the information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, information may also be referred to as second information, and similarly, second information may also be referred to as information, without departing from the scope of the present specification. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The description aims to provide an infringement detection scheme based on a block chain, wherein infringement detection is refined to a local area, and if a local feature vector of an image to be detected is similar to a local feature vector of an original image which is proved in the block chain, the infringement area which infringes the original image exists in the image to be detected. In this manner, locally infringing images, such as picture-in-picture, image stitching, and the like, may be identified.
On the other hand, as the data stored in the block chain has the characteristic of being not falsifiable, a credible local characteristic library can be constructed after the local characteristic vectors extracted from the local sub-images of the original image are stored in the block chain; therefore, the infringement detection result of the infringement detection of the image to be detected based on the local feature library stored on the block chain is also credible; and the uplink of the infringement detection result can be prevented from being tampered, so that the security of the infringement detection result is ensured.
The blockchain described in this specification may specifically include a private chain, a common chain, a federation chain, and the like, and is not particularly limited in this specification. Node devices in the block chain can be added without limitation, and each node device can synchronize a system time to ensure timeliness of execution of the intelligent contract.
It should be noted that the Transaction (Transaction) described in this specification refers to a piece of data that is created by a client of the blockchain and needs to be finally distributed to the data storage system of the blockchain.
Transactions in a blockchain, generally have a narrow sense of transaction and a broad sense of transaction score. A narrowly defined transaction refers to a transfer of value issued by a user to a blockchain; for example, in a conventional bitcoin blockchain network, the transaction may be a transfer initiated by the user in the blockchain. The broad transaction refers to a piece of business data with business intention, which is issued to the blockchain by a user; for example, an operator may build a federation chain based on actual business requirements, relying on the federation chain to deploy some other types of online business unrelated to value transfer (e.g., broadly classified as query business, call business, etc.), and in such federation chain, the transaction may be a business message or business request with a business intent issued by a user in the federation chain.
The client may include any type of upper layer application that uses the bottom layer service data stored in the blockchain as a data support to implement a specific service function.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating a network environment related to a blockchain according to the present disclosure.
In a network environment as shown in fig. 1, may include a client-side computing device 101, a server-side 102, and at least one blockchain system; such as blockchain system 103, blockchain system 104, and blockchain system 105.
In one embodiment, the client-side computing device 101, may include a variety of different types of client-side computing devices; for example, the client side terminal device may include devices such as a PC terminal device, a mobile terminal device, an internet of things device, and other forms of smart devices with certain computing capabilities, and so on.
In one embodiment, at least some of the computing devices in the client-side terminal device 101 may be coupled to the server-side 102 through various communication networks; for example, the device 3 shown in fig. 1 is coupled to the server side 102.
It is understood that some of the client-side terminal devices 101 may not be coupled to the server-side terminal device 102, but may be directly coupled to the blockchain system as blockchain nodes through various communication networks; for example, the apparatus 4 shown in fig. 1 may be coupled to the blockchain system as a blockchain link point.
Wherein, the communication network may comprise a wired and/or wireless communication network; for example, the Network may be a Local Area Network (LAN), a Wide Area Network (WAN), the internet, or a combination thereof, implemented based on a wired access Network or a wireless access Network provided by an operator, such as a mobile cellular Network.
In one embodiment, the client-side computing device 101, may also include one or more user-side servers; such as the device 5 shown in fig. 1. At least a portion of the computing devices in the client-side terminal device 101 may be coupled to the user-side server, and the user-side server may be further coupled to the server 102; for example, devices 1 and 2 shown in fig. 1 are coupled to device 5, and device 5 is further coupled to server side 102.
In an embodiment, the user-side server may be implemented by a service entity that establishes a user account system; the service entity may include an operation entity providing various service bearers for online and/or offline services to a user;
the service carrier may include a service carrier in a software form, and may also include a service carrier in a hardware form.
In one embodiment, the service carrier may include various client software providing online internet services; such as a website, web page, APP, etc.
In an embodiment, the service carrier may also include various intelligent devices deployed offline and capable of providing offline services; for example, intelligent express cabinets are deployed in residential areas, office areas, and public places.
Correspondingly, the operation entity may include an operator corresponding to the service bearer; for example, the operation entity may include an individual, an organization, a company, an enterprise, and the like that operate and manage the service carrier.
In one embodiment, the server side 102 may also be coupled to one or more blockchain systems through various communication networks; for example, the server side 102 shown in fig. 1 may be coupled to the blockchain system 103, the blockchain system 104, and the blockchain system 105, respectively, and so on.
In one embodiment, each blockchain system may maintain one or more blockchains (e.g., public blockchains, private blockchains, federation blockchains, etc.) and include a plurality of blockchain nodes for carrying the one or more blockchains; for example, a block chain node 1, a block link point 2, a block link point 3, a block link point 4, a block link point i, etc., as shown in fig. 1, may collectively carry one or more block chains. And cross-chain data access can be performed among the blockchains contained in each blockchain system and among the blockchain systems.
In one embodiment, the block link points may include full nodes and light nodes. The whole node can download the blockchain transaction contained in each block in the blockchain in a whole amount, and can perform consensus verification on the blockchain transaction contained in each blockchain according to the carried blockchain consensus algorithm.
And the light node may not download the complete blockchain, but may only download the data of the block header of each block in the blockchain, and use the data contained in the block header as a verification root for verifying the authenticity of the blockchain transaction. Light nodes may attach to full nodes to access more functions of the blockchain.
For example, each blockchain node in the blockchain system 103 shown in fig. 1 may be a full node; the device 4 shown in fig. 1, which is directly coupled to the blockchain system, may be attached as a light node to each full node in the blockchain system 103.
In one embodiment, a block link point may be a physical device, or may be a virtual device implemented in a server or a server cluster; for example, the block-node device may be a physical host in a server cluster, or may be a virtual machine created after a virtualization technology is performed on a server or a hardware resource carried by the server cluster. Each blockchain node may be coupled together by various types of communication methods (e.g., TCP/IP) to form a network to carry one or more blockchains.
In one embodiment, the server 102 may include a BaaS platform (also referred to as a BaaS cloud) for providing a Blockchain as a Service (BaaS). The BaaS platform can provide a pre-programmed software mode for activities (such as subscription and notification, user verification, database management and remote update) occurring on the blockchain, and provides a simple and easy-to-use, one-key deployment, quick verification and flexible and customizable blockchain service for client-side computing equipment coupled with the BaaS platform, so that the application development, test and online of blockchain services can be accelerated, and landing of blockchain business application scenes of various industries can be facilitated.
For example, in one example, a BaaS platform may provide software such as MQ (Message Queue) services; the client-side computing equipment coupled with the BaaS platform can subscribe an intelligent contract deployed on a certain block chain in a block chain system coupled with the BaaS platform and trigger a contract event generated on the block chain after execution; and the BaaS platform can monitor the event generated on the block chain after the intelligent contract is triggered to execute, and then add the contract event into the message queue in the form of notification message based on the software related to MQ service, so that the client-side computing device subscribing the message queue can obtain the notification related to the contract event.
In one embodiment, the BaaS platform may also provide enterprise-level platform services based on blockchain technology to help enterprise-level customers construct a secure and stable blockchain environment and easily manage deployment, operation, maintenance, and development of blockchains.
For example, in one example, the BaaS platform may implement rich security policies and multi-tenant isolation environments based on cloud technology, provide advanced security protection based on chip encryption technology, provide highly reliable data storage based on high availability end-to-end services that can be quickly extended without interruption;
in another example, enhanced management functionality may also be provided to assist customers in building enterprise-level blockchain network environments; and, local support can also be provided for standard blockchain applications and data, supporting mainstream open source blockchain technologies such as Hyperhedger Fabric and Enterprise Ethereum-Quorum, to build an open and inclusive technology ecosystem.
After the above-mentioned blockchain technique is introduced, the following description will describe a blockchain-based infringement detection method provided in the present specification.
Referring to fig. 2, fig. 2 is a flowchart illustrating a block chain-based infringement detection method according to an embodiment of the present disclosure, where the method may be applied to a server. The server side can be the server side 102 shown in the aforementioned fig. 1; or may be a client (e.g., device 4) directly connected to the blockchain as shown in fig. 1.
Specifically, the method illustrated in fig. 2 may include the following steps:
step 210: and carrying out significance detection on the image to be detected to obtain at least one local sub-image.
The image to be detected can be an image work finished by a user; generally, after completing the original image works, the user can upload the image works to an original platform for registration, and the original platform can be the server.
In implementation, the server may detect a Saliency region in an image to be detected based on a Saliency Detection (salience Detection) technology, and crop out the Saliency region to obtain N local sub-images.
The significance detection technology can adopt algorithms commonly used in the industry, such as machine learning models of detection networks, Mask RCNN networks and the like. These machine learning models typically require model training in advance through a large number of labeled sample images in which regions with salient features and label information represented by the regions are labeled. For example, for a face image sample, the facial region of five sense organs and the name label of the five sense organs each region represents may be labeled.
Usually, a large amount of sample images can train the machine learning model, and each parameter in the model can be optimized through continuous calculation, so that the identification accuracy of the model is higher and higher. When the model training reaches the preset requirement (for example, the accuracy exceeds the threshold value, and the iteration number exceeds the preset number), the trained model can be used. At this time, the image to be detected is input into the model for calculation, and then the local sub-image with the significant characteristic can be output.
Step 220: local features are extracted from the at least one local sub-image, and a local feature vector is constructed based on the local features.
After N local sub-images are obtained, local feature vectors can be extracted from each local sub-image, and the local feature vectors of the N local sub-images can be obtained.
Feature extraction here can use networks including, but not limited to, deep feature extraction, such as VGG models, ResNet, MobileNet; or SIFT, SURF, ORB and other feature extraction methods.
Similar to the model training method for the local sub-images, the model for feature extraction also needs to be trained in advance. And will not be described in detail at this time.
Step 230: carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
The block chain is verified to have an original image subjected to original authentication and a local feature vector constructed by local features extracted from a local sub-image of the original image; the local feature vectors of these original images as original feature vectors may be used to provide a trusted local feature library for infringement detection.
The local sub-image of the original image and the local feature vector in the local sub-image are obtained in the same way as the image to be detected.
In practical applications, whether public, private, or alliance, it is possible to provide the functionality of a Smart contract (Smart contract). An intelligent contract on a blockchain is a contract on a blockchain that can be executed triggered by a transaction. An intelligent contract may be defined in the form of code.
The intelligent contract can be independently executed at each node in the blockchain network in a specified mode, and all execution records and data are stored on the blockchain, so that after the transaction is executed, transaction certificates which cannot be tampered and lost are stored on the blockchain.
When the intelligent contract is implemented, the business logic of the intelligent contract can be issued to the blockchain in the form of codes, so that the blockchain creates the corresponding intelligent contract, and the intelligent contract can access the codes after being called to implement the execution of the business logic.
In this specification, however, intelligent contracts containing code for infringement detection logic may be issued into blockchains.
In one implementation, the server may invoke infringement detection logic declared in an intelligent contract issued to the blockchain, and perform infringement detection on the local feature vector and an original feature vector validated in the blockchain.
In this way, the server can be used as a node of the block chain to directly call the intelligent contract locally for infringement detection.
In another implementation, the server may issue the local feature vector to the blockchain as a transaction of the blockchain; so that the accounting node in the block chain responds to the transaction, calls infringement detection logic declared in an intelligent contract issued in the block chain, and carries out infringement detection on the basis of the local feature vector and the original feature vector stored in the block chain.
In this way, the server may initiate a transaction for infringement detection, so that the accounting node in the blockchain invokes the intelligent contract for infringement detection.
In an embodiment, before the step 230, the method may further include:
performing dimensionality reduction on the local feature vector;
then carrying out infringement detection based on the local feature vector after dimensionality reduction and the original feature vector of evidence stored in the block chain; the original feature vector may also be the original feature vector after dimensionality reduction.
The dimension reduction processing may use a dimension reduction algorithm such as a Principal Component Analysis (PCA) algorithm or Singular Value Decomposition (SVD).
In this embodiment, the data amount of the local feature vector can be reduced by dimension reduction, so that the calculation amount consumed in the infringement detection calculation can be reduced, and thus the detection efficiency can be improved due to the reduction of the calculation amount.
In the foregoing step 230, the local feature vectors and the original feature vectors of the block chain evidence are subjected to infringement detection through steps a1 to a 2.
Step A1: carrying out similarity calculation on the local feature vector and the original feature vector stored in the block chain;
during implementation, the N local feature vectors can be respectively compared with the original feature vectors, so that each local feature vector can recall M original feature vectors (M represents the number of the original feature vectors);
then, the N × M feature vector groups (1 local feature vector and 1 original feature vector) are screened to determine that similar original feature vectors exist.
In one implementation, feature aggregation may be performed on original feature vectors of the same original picture to obtain an original feature aggregation vector;
performing feature aggregation on the local feature vectors to obtain local feature aggregation vectors;
calculating the similarity of the local feature aggregation vector and each original feature aggregation vector;
and determining the original feature aggregation vector with the similarity larger than a threshold value.
In another implementation, original feature vectors similar to the local feature vectors can be screened out by means of similar feature number threshold screening, score confidence interval screening and the like.
The similar feature number threshold screening may be performed by calculating the number of similar features in the local feature vector and the original feature vector, and when the number of similar features exceeds a certain threshold, it may be determined that the original feature vector is similar to the local feature vector.
Step A2: when the similarity between the local feature vector and the original feature vector is not greater than a threshold value, determining that the infringement detection result is not infringement; and issuing the related information of the local sub-image to the block chain for evidence storage.
If the original characteristic vector similar to the local characteristic vector does not exist, the to-be-detected image is not similar to the original image of the evidence stored in the block chain, so that the to-be-detected image is the original image and does not infringe the registered original image.
After determining that the image to be detected is not infringing, the related information of the image to be detected and the local sub-image can be stored into the block chain.
Wherein the related information of the local sub-image comprises: the local feature vector (as new original feature information), the corresponding relationship between the local feature vector and the image to be detected, and the position information of the local sub-image in the image to be detected.
The local feature vector of the to-be-detected image belonging to the original image is stored in the block chain, so that the content of a local feature library stored in the block chain is perfected, and the credible local feature information of the original image is provided for subsequent infringement detection.
Step A3: and when the similarity between the local feature vector and the original feature vector is larger than a threshold value, determining the infringement detection result as infringement.
When the infringement detection result is infringement, the infringement information is stored to the block chain; wherein the infringement information includes:
an infringement area in the image to be detected and the original image; the infringement region comprises local sub-images, wherein the local feature vectors with the similarity larger than a threshold value correspond to the local sub-images in the image to be detected, and the original feature vectors with the similarity larger than the threshold value correspond to the local sub-images in the original image.
The purpose of fixing the certificate is achieved by storing the infringement information into the block chain. When the infringement dispute occurs, the infringement information of the block chain deposit evidence can be used as the right-maintaining evidence of the original creator, so that the right-maintaining success rate is improved.
By thinning the infringement detection to a local area, if the local feature vector of the image to be detected is similar to the local feature vector of the original image which is proved to exist in the block chain, the infringement area which infringes the original image exists in the image to be detected. In this manner, locally infringing images, such as picture-in-picture, image stitching, and the like, may be identified.
On the other hand, as the data stored in the block chain has the characteristic of being not falsifiable, a credible local characteristic library can be constructed after the local characteristic vectors extracted from the local sub-images of the original image are stored in the block chain; therefore, the infringement detection result of the infringement detection of the image to be detected based on the local feature library stored on the block chain is also credible; and the uplink of the infringement detection result can be prevented from being tampered, so that the security of the infringement detection result is ensured.
Referring to fig. 3 again, fig. 3 is a flowchart illustrating a block chain-based infringement detection method according to an embodiment of the present disclosure, where the method may be applied to a server corresponding to a client; wherein the client includes a decentralized client (for example, the device 3 shown in fig. 1) and the server includes a blockchain, i.e., a service platform (for example, the server 102 shown in fig. 1). The method can comprise the following steps:
step 310: and receiving the image to be detected uploaded by the client.
The image to be detected can refer to an image work finished by a user; generally, after completing the original image works, the user can upload the image works to an original platform for registration, and the original platform can be the server.
Step 320: carrying out significance detection on an image to be detected to obtain at least one local sub-image;
this step is the same as step 210 described in the embodiment of fig. 2, and reference may be made to the content described in step 210, which is not described herein again.
Step 330: extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
this step is the same as step 220 described in the embodiment of fig. 2, and reference may be made to the content described in step 220, which is not described herein again.
Step 340: carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
this step is similar to the step 230 described in the embodiment of fig. 2, and reference may be made to the content described in the step 230, which is not described herein again.
Step 350: and when the infringement detection result is that the infringement is not infringed, the image to be detected is stored and verified to a block chain.
When the infringement detection result is that the infringement is not infringed, the image to be detected is indicated to be an original image, and therefore the image to be detected can be stored into the block chain as the original image.
The embodiment provides an infringement detection scheme based on a block chain, wherein infringement detection is refined to a local area, and local infringement detection is performed on a local significant area in an image to be detected by using a local feature vector of an original image which is stored in the block chain. In this manner, locally infringing images, such as picture-in-picture, image stitching, and the like, may be identified.
Because the image to be detected does not locally infringe the original work, the image to be detected can be used as the original work and stored in the block chain, and the original information of the original work is recorded by utilizing the characteristic that the block chain cannot be tampered (for example, the winding time can be regarded as the original time of the work); the original works stored with the certificate through the block chain can guarantee the original rights and interests. For example, the recorded original information may be used as proof of right-to-be-protected.
An embodiment of the present specification further provides a block chain-based infringement detection method based on a conventional block chain. The method is written from the blockchain side, and nodes in the blockchain are used as execution subjects. The method may comprise the steps of:
step B1: receiving a calling transaction for carrying out infringement detection on an image to be detected; the calling transaction comprises a local feature vector constructed by local features extracted from local sub-images of the image to be detected, and the local sub-images comprise image areas obtained by performing significance detection on the image to be detected.
The significance detection, the local feature extraction, and the like in this step have been described in the foregoing embodiments, and are not described herein again.
It is worth mentioning that in some embodiments, invoking the transaction may include only the images to be detected, and then the saliency detection, local feature extraction, and feature vector construction may invoke the infringement detection logic in the smart contract to perform.
Step B2: calling infringement detection logic declared in an intelligent contract issued in a block chain in response to the calling transaction, and carrying out infringement detection on the basis of the local feature vector and an original feature vector stored in the block chain; wherein the original feature vector comprises a local feature vector extracted from a local sub-image of the original image.
For the detection of infringement in this step, reference may be made to the example in the foregoing embodiment, and details are not described here.
Step B3: and storing the infringement detection result to the block chain.
For the existence of an original feature vector similar to the local feature vector, the infringement detection result comprises: an infringement area in the image to be detected and the original image; the infringement region comprises similar local feature vectors and original feature vectors which correspond to local sub-images in the image to be detected and the original image.
By thinning the infringement detection to a local area, if the local feature vector of the image to be detected is similar to the local feature vector of the original image which is proved to exist in the block chain, the infringement area which infringes the original image exists in the image to be detected. In this manner, locally infringing images, such as picture-in-picture, image stitching, and the like, may be identified.
On the other hand, as the data stored in the block chain has the characteristic of being not falsifiable, a credible local characteristic library can be constructed after the local characteristic vectors extracted from the local sub-images of the original image are stored in the block chain; therefore, the infringement detection result of the infringement detection of the image to be detected based on the local feature library stored on the block chain is also credible; and the uplink of the infringement detection result can be prevented from being tampered, so that the security of the infringement detection result is ensured.
Corresponding to the embodiment of the method for detecting infringement based on the block chain, the present specification also provides an embodiment of a device for detecting infringement based on the block chain. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and as a logical device, the device is formed by reading corresponding computer business program instructions in the nonvolatile memory into the memory for operation through the processor of the device in which the device is located. From a hardware aspect, as shown in fig. 4, the hardware structure diagram of the device where the block chain-based infringement detection apparatus is located in this specification is shown, except for the processor, the network interface, the memory, and the nonvolatile memory shown in fig. 4, the device where the apparatus is located in the embodiment may also include other hardware generally according to an actual function of the block chain-based infringement detection, which is not described again.
Referring to fig. 5, a block diagram of an apparatus for block chain-based piracy detection according to an embodiment of the present disclosure is shown, where the apparatus corresponds to the embodiment shown in fig. 2, and the apparatus includes:
a saliency detection unit 510, configured to perform saliency detection on an image to be detected to obtain at least one local sub-image;
a feature extraction unit 520, which extracts local features from the at least one local sub-image and constructs a local feature vector based on the local features;
an infringement detection unit 530, which performs infringement detection on the local feature vector and the original feature vector stored in the block chain to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
Optionally, in the infringement detection unit 530, the performing infringement detection on the local feature vector and an original feature vector verified in the block chain includes:
and calling infringement detection logic declared in an intelligent contract issued in the block chain, and carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain.
Optionally, in the infringement detection unit 530, performing infringement detection on the local feature vector and an original feature vector that exists in the block chain, where the infringement detection includes:
the calculating subunit is used for calculating the similarity between the local feature vector and the original feature vector stored in the block chain;
and the determining subunit determines that the infringement detection result is infringement when the similarity between the local feature vector and the original feature vector is greater than a threshold.
Optionally, the calculating subunit includes:
performing feature aggregation on original feature vectors of the same original image to obtain original feature aggregation vectors; performing feature aggregation on the local feature vectors to obtain local feature aggregation vectors; calculating the similarity of the local feature aggregation vector and each original feature aggregation vector; and determining the original feature aggregation vector with the similarity larger than a threshold value.
Optionally, the apparatus further comprises:
the evidence storing subunit is used for storing the infringement information into the block chain when the infringement detection result is infringement; wherein the infringement information includes: an infringement area in the image to be detected and the original image; the infringement region comprises local sub-images, wherein the local feature vectors with the similarity larger than a threshold value correspond to the local sub-images in the image to be detected, and the original feature vectors with the similarity larger than the threshold value correspond to the local sub-images in the original image.
Optionally, the apparatus further comprises:
the evidence storing subunit determines that the infringement detection result is not infringement when the similarity between the local feature vector and the original feature vector is not greater than a threshold value; and issuing the related information of the local sub-image to the block chain for evidence storage.
Optionally, the information related to the local sub-image includes:
the local feature vector, the corresponding relation between the local feature vector and the image to be detected, and the local sub-image correspond to the position information in the image to be detected.
Referring to fig. 6, a block diagram of an apparatus for block chain-based piracy detection according to an embodiment of the present disclosure is shown, where the apparatus corresponds to the embodiment shown in fig. 3, and the apparatus includes:
the image receiving unit 610 receives an image to be detected uploaded by a client;
a saliency detection unit 620, which performs saliency detection on an image to be detected to obtain at least one local sub-image;
a feature extraction unit 630, extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
the infringement detection unit 640 performs infringement detection on the local feature vectors and original feature vectors stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
the image verification unit 650 verifies the image to be detected to the block chain when the infringement detection result is that the infringement is not infringed.
Optionally, the image verification unit 650 includes:
and when the infringement detection result is that the infringement is not infringed, storing the local characteristic vector of the image to be detected, the corresponding relation between the local characteristic vector and the image to be detected and the position information of the local sub-image in the image to be detected as original information into a block chain.
Optionally, the apparatus is applied to a server corresponding to the client; the client comprises a decentralized client, and the server comprises a block chain, namely a service platform.
An embodiment of the present specification provides a block chain-based infringement detection apparatus, including:
the receiving unit is used for receiving a calling transaction for carrying out infringement detection on the image to be detected; wherein the call transaction comprises the image to be detected;
the determining unit is used for responding to the calling transaction, calling infringement detection logic declared in an intelligent contract issued in a block chain, and determining a local sub-image in the image to be detected; the local sub-images comprise image areas with significant characteristics in the image to be detected;
an extraction unit that extracts local feature vectors from the local sub-images;
the detection unit is used for carrying out infringement detection on the basis of the local feature vector and an original feature vector stored in the block chain; wherein the original feature vector comprises a local feature vector extracted from a local sub-image of the original image;
and the evidence storing unit is used for storing the infringement detection result into the block chain.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in the specification. One of ordinary skill in the art can understand and implement it without inventive effort.
This specification also provides an electronic device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform any of the above block chain based infringement detection methods.
In the above embodiments of the electronic device, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the aforementioned memory may be a read-only memory (ROM), a Random Access Memory (RAM), a flash memory, a hard disk, or a solid state disk. The steps of a method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiment of the electronic device, since it is substantially similar to the embodiment of the method, the description is simple, and for the relevant points, reference may be made to part of the description of the embodiment of the method.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the specification following, in general, the principles of the specification and including such departures from the present disclosure as come within known or customary practice within the art to which the specification pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.
It will be understood that the present description is not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.
Claims (13)
1. A block chain based infringement detection method, the method comprising:
carrying out significance detection on an image to be detected to obtain at least one local sub-image;
extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
2. The method of claim 1, wherein the detecting infringement of the local feature vector with an original feature vector certified in a block chain comprises:
and calling infringement detection logic declared in an intelligent contract issued in the block chain, and carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain.
3. The method according to claim 1 or 2, wherein the infringing detection of the local feature vector and an original feature vector certified in a block chain comprises:
carrying out similarity calculation on the local feature vector and the original feature vector stored in the block chain;
and when the similarity between the local feature vector and the original feature vector is larger than a threshold value, determining the infringement detection result as infringement.
4. The method of claim 3, further comprising:
performing feature aggregation on original feature vectors of the same original image to obtain original feature aggregation vectors;
performing feature aggregation on the local feature vectors to obtain local feature aggregation vectors;
calculating the similarity of the local feature aggregation vector and each original feature aggregation vector;
and determining the original feature aggregation vector with the similarity larger than a threshold value.
5. The method of claim 3, further comprising:
when the infringement detection result is infringement, the infringement information is stored to the block chain; wherein the infringement information includes:
an infringement area in the image to be detected and the original image; the infringement region comprises local sub-images, wherein the local feature vectors with the similarity larger than a threshold value correspond to the local sub-images in the image to be detected, and the original feature vectors with the similarity larger than the threshold value correspond to the local sub-images in the original image.
6. The method of claim 3, further comprising:
when the similarity between the local feature vector and the original feature vector is not greater than a threshold value, determining that the infringement detection result is not infringement;
and issuing the related information of the local sub-image to the block chain for evidence storage.
7. The method of claim 6, the information related to the local sub-image comprising:
the local feature vector, the corresponding relation between the local feature vector and the image to be detected, and the local sub-image correspond to the position information in the image to be detected.
8. A block chain based infringement detection method, the method comprising:
receiving an image to be detected uploaded by a client;
carrying out significance detection on an image to be detected to obtain at least one local sub-image;
extracting local features from the at least one local sub-image, and constructing a local feature vector based on the local features;
carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
and when the infringement detection result is that the infringement is not infringed, the image to be detected is stored and verified to a block chain.
9. The method according to claim 8, wherein when the infringement detection result is that the infringement is not infringed, the verifying the image to be detected to a block chain comprises:
and when the infringement detection result is that the infringement is not infringed, storing the local characteristic vector of the image to be detected, the corresponding relation between the local characteristic vector and the image to be detected and the position information of the local sub-image in the image to be detected as original information into a block chain.
10. The method of claim 8, applied to a server corresponding to the client; the client comprises a decentralized client, and the server comprises a block chain, namely a service platform.
11. An apparatus for block chain based infringement detection, the apparatus comprising:
the saliency detection unit is used for carrying out saliency detection on the image to be detected to obtain at least one local sub-image;
the characteristic extraction unit is used for extracting local characteristics from the at least one local sub-image and constructing a local characteristic vector based on the local characteristics;
the infringement detection unit is used for carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain so as to determine an infringement detection result; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image.
12. An apparatus for block chain based infringement detection, the apparatus comprising:
the image receiving unit is used for receiving the image to be detected uploaded by the client;
the saliency detection unit is used for carrying out saliency detection on the image to be detected to obtain at least one local sub-image;
the characteristic extraction unit is used for extracting local characteristics from the at least one local sub-image and constructing a local characteristic vector based on the local characteristics;
the infringement detection unit is used for carrying out infringement detection on the local feature vector and the original feature vector stored in the block chain; the original feature vector comprises a local feature vector constructed by local features extracted from local sub-images of the original image;
and the image evidence storing unit is used for storing the image to be detected into the block chain when the infringement detection result is that the infringement is not infringed.
13. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any of the preceding claims 1-10.
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| PCT/CN2022/107797 WO2023045534A1 (en) | 2021-09-23 | 2022-07-26 | Blockchain-based infringement detection |
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