CN114359590A - NFT image work infringement detection method and device and computer storage medium - Google Patents
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Abstract
An embodiment of the present specification provides an NFT image work infringement detection method, an NFT image work infringement detection device, and a computer storage medium, where the method may include: comparing the NFT work with image characteristics of a plurality of images in an image database to obtain the similarity between the NFT work and each image in the image database; if the similarity between the NFT work and any image in the image database is greater than a preset similarity threshold, it may be determined that the NFT work is infringing.
Description
Technical Field
The embodiment of the specification relates to the technical field of computers, in particular to an NFT image work infringement detection method and device and a computer storage medium.
Background
With the rapid development of internet technology, a large number of image originals have been widely used in various fields of society, but the problem of infringement of corresponding originals is also becoming more serious, for example, in order to earn profit in the open digital art Non-homogeneous Token (NFT) market, some lawbreakers often make similar digital image art to earn a bout of violence by means of image editing and the like.
Therefore, legal disputes due to infringement of NFT works are increasing, and the rights and interests of originators are infringed. In view of this, how to detect infringement of NFT works becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the specification provides an infringement detection method and device for NFT image works and a computer storage medium, which can effectively detect whether NFT works infringe or not.
In a first aspect, embodiments of the present specification provide a method for infringement detection of NFT image pieces of non-homogeneous tokens, the method comprising:
comparing the image work of the NFT to be detected with the image characteristics of a plurality of images in an image database to obtain the similarity between the image work of the NFT to be detected and the images in the image database;
and if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
In a second aspect, the present specification provides a non-homogeneous token NFT image work infringement detection apparatus, the apparatus comprising:
the comparison module is used for comparing the NFT image work to be detected with the image characteristics of a plurality of images in an image database to obtain the similarity between the NFT image work to be detected and the images in the image database;
the determining module is configured to determine that the NFT image work to be detected infringes if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold.
In a third aspect, the illustrative embodiments provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to carry out the above-mentioned method steps.
In a fourth aspect, embodiments of the present specification provide an electronic device, which may include: a processor and a memory;
wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by some embodiments of the present description brings beneficial effects at least including:
the method comprises the steps that the similarity between the NFT image work to be detected and images in an image database can be obtained by comparing the image features of the NFT image work to be detected with the image features of a plurality of images in the image database; and if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes. Therefore, on one hand, the infringement detection of the NFT works is completed by using an image feature comparison mode, so that the situation that the infringement cannot be detected due to slight modification of the NFT works, such as scaling or editing of a small number of pixels, is avoided, and the accuracy of the infringement detection is improved. On the other hand, the creator is not required to provide a large amount of evidence to prove the right information of the image, a large amount of manpower and material resources are saved, tedious work such as manual examination and uploading of examination results is avoided, and the infringement detection efficiency is also improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1a is a diagram of an NFT image work to be detected provided in an embodiment of the present specification;
FIG. 1b is an image of hot news provided by an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for detecting infringement of an NFT image work according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another NFT image work infringement detection method provided in an embodiment of the present specification;
fig. 4a is a diagram of yet another NFT image work to be detected provided by an embodiment of the present specification;
fig. 4b is a partial feature diagram of another NFT image work to be detected according to an embodiment of the present disclosure;
fig. 4c is another partial feature diagram of another NFT image work to be detected according to an embodiment of the present disclosure;
fig. 5 is a schematic flowchart of a further NFT image product infringement detection method provided in an embodiment of the present specification;
fig. 6 is an application scenario diagram of an NFT image work infringement detection method provided in an embodiment of the present specification;
fig. 7 is a schematic structural diagram of an NFT image work infringement detection apparatus provided in an embodiment of the present specification;
fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification.
Detailed Description
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.
In the description herein, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present specification can be understood in specific cases by those of ordinary skill in the art. Further, in the description of the present specification, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
With the rapid development of blockchain technology, digital artwork NFT transactions and the like over blockchain infrastructure have also come to the opportunity of development. Specifically, the NFT is a unit of data on a digital book called a blockchain, and each NFT can represent a unique digital datum. Because they are not interchangeable, a non-homogeneous token may represent a digital file such as a drawing, sound, movie, in-game item, or other form of creative work. Further, the NFT may be used for verification of ownership and to allow NFT image work characterized by the NFT to be traded and sold on a digital marketplace.
Possibly, the NFT image work trading platform may include an image work uploaded by a user and a corresponding NFT for the image work. For example, OpenSea is currently the largest global integrated NFT graphic work trading platform on which users can cast, display, trade, and auction NFTs.
The owner of ownership of the NFT image work shown in fig. 1a is a queen, and the queen can trade the NFT image work on the NFT image work trading platform through the NFT, that is, the ownership of the NFT image work is sold to other people. In the hot news shown in fig. 1b, the owner of the image ownership is a small sheet, and it can be found from observing fig. 1a and 1b that the content in the two images is the same, and the difference between them is that fig. 1a adds a virtual frame on the image work of fig. 1b, and accordingly, the NFTs generated in fig. 1a and 1b are different. Therefore, if the real holder of the NFT image work is a sheetlet, the queen infringes the ownership of the sheetlet when the NFT image work transaction platform transacts the NFT work. Therefore, the present specification provides a method for performing infringement detection on an NFT image work transaction platform, so as to reduce infringement disputes occurring on the NFT image work transaction platform.
Next, the NFT image work infringement detection method provided by one or more embodiments of the present specification is described with reference to the NFT image work corresponding to the NFT described in fig. 1a and 1 b. The method for detecting infringement of the NFT image works is used for a non-homogeneous token NFT image work trading platform.
In one or more embodiments of the present description, fig. 2 is a schematic flow chart of an NFT image work infringement detection method. As shown in fig. 2, the NFT image product infringement detection method may include the following steps:
The NFT image work to be detected in the embodiment of the present specification is an NFT work.
Optionally, the NFT image work to be detected is an NFT image work in an NFT image work transaction platform. Because the number of the NFT image works on the NFT image work transaction platform is huge, in order to reduce the data volume of the piracy detection, further, the NFT image works to be detected may be the NFT image works of which the liveness in the NFT image work transaction platform is higher than a first preset threshold. Wherein, the liveness can be represented by the transaction times, the browsing times and the like of the NFT image works. The first preset threshold may be, for example, 40%, that is, the NFT image work to be detected is an NFT image work with a front traffic ranking of 40% in the NFT image work transaction platform, or the image to be detected is an NFT image work with a front traffic ranking of 40% in the NFT image work transaction platform, or the like.
Optionally, the NFT image work to be detected is an NFT image work to be uploaded to an NFT image work transaction platform. And if the other image works which are not infringed by the NFT image work to be detected are detected, namely the similarity between the NFT image work to be detected and any image in the image database is smaller than or equal to a preset similarity threshold, allowing the NFT image work to be uploaded to an NFT image work transaction platform.
Specifically, the images in the image database are images in hot event information, and the hot event information is event information with a browsing volume greater than a second preset threshold. The second preset threshold is used as the preset browsing amount. For example, the second preset threshold is 10 ten thousand. Specifically, the hotspot event information may be an event that the browsing volume of each website is greater than a second preset threshold. Specifically, the data on each website can be acquired in a web crawler manner. It is known that hot spot events are typically time sensitive, and the heat of the event may decrease over time. Thus, the images in the image database may be updated at intervals. Such as but not limited to weekly updates, or monthly updates, or every three months, etc. The browsing volume of the hot spot event is the total browsing volume in the period of time. Specifically, the hot event may include hot news expressed in text and picture forms, hot video delivered in video forms, hot audio broadcasted in audio additional picture forms, and the like.
Specifically, in the embodiments of the present description, technical means such as Structural Similarity (SSIM), cosine Similarity, histogram, and normalized mutual information may be used to measure the Similarity between the NFT image work to be detected and each image in the image database.
Specifically, the structural similarity is a fully-referenced image quality evaluation index, which can measure the image similarity from three aspects of brightness, contrast and structure. In practical application, an image can be blocked by using a sliding window, the total number of blocks is N, the influence of the shape of the window on the blocks is considered, the mean value, the variance and the covariance of each window are calculated by adopting Gaussian weighting, then the structural similarity SSIM of the corresponding block is calculated, and finally the mean value is used as the structural similarity measurement of two images, namely the average structural similarity SSIM. The cosine similarity represents the similarity between the NFT image work to be detected and each image in the image database by calculating the cosine distance between the vectors. The histogram can describe the global distribution of colors in one image, and the similarity between the work of the NFT image to be detected and each image in the image database can be determined by comparing the color distribution. The normalized mutual information can be understood as the information amount of the images in the image database contained in the NFT image work to be detected, the larger the value of the normalized mutual information is, the higher the similarity between the images is, the better the registration accuracy and the higher reliability are when the gray scale levels of the two images are similar.
And 204, if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
The preset similarity threshold may be understood as a threshold for determining whether the NFT image work to be detected has infringement risk. For example, the value range of the similarity is between 0 and 1, and the preset similarity threshold may be set to 0.7. And under the condition that the similarity between the NFT image work to be detected and any image in the image database is greater than 0.7, determining that the NFT image work to be detected infringes the image in the image database.
Further, in order to fix the evidence of infringement of the NFT image work to be detected, after the infringement of the NFT image work to be detected is determined in the embodiment of the present specification, the NFT image work to be detected and an infringement object (that is, an image in the image database, whose similarity to the NFT image work to be detected is greater than a preset similarity threshold) and a specific similarity between the NFT image work and the infringement object may be uploaded to the block chain, so that the evidence fixation is completed by using the characteristic that the block chain cannot be tampered with, and the detection result is prevented from being modified by others. Specifically, if the NFT image work to be detected is the NFT image work to be uploaded to the NFT image work transaction platform, if the similarity between the NFT image work to be detected and any one image in the image database is less than or equal to the preset similarity threshold, that is, the image work in the infringement-free image database of the NFT image work to be detected, the NFT image work to be detected may be uploaded to the NFT image work transaction platform in the embodiment of the present specification. For example, if the similarity between any one image and the NFT image work of the user is not greater than the preset similarity threshold value of 0.7 in the image database, that is, the similarity between each image in the image database and the NFT image work to be uploaded to the NFT image work transaction platform is less than or equal to the preset similarity threshold value, it may be determined that there is no infringement behavior in the NFT image work to be uploaded to the NFT image work transaction platform, the NFT image work transaction platform may receive the NFT image work, and the user may perform a transaction or auction on the block chain by using the NFT corresponding to the NFT image work on the NFT image work transaction platform.
The method includes the steps that image characteristics of a to-be-detected NFT image work and a plurality of images in an image database are compared, and the similarity between the to-be-detected NFT image work and the images in the image database is obtained; and if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes. Therefore, on one hand, the embodiment of the specification completes infringement detection on the NFT product by using an image feature comparison mode, so that the detection infringement detection failure caused by slight modification, such as scaling or editing of a small number of pixels, on the NFT product is avoided, and the accuracy of infringement detection is improved. On the other hand, the creator is not required to provide a large amount of evidence to prove the right information of the image, a large amount of manpower and material resources are saved, tedious work such as manual examination and uploading of examination results is avoided, and the infringement detection efficiency is also improved.
The image features in the embodiments of the present specification may include global features and local features. Fig. 3 is a schematic flow chart of an NFT image work infringement detection method. As shown in fig. 3, the NFT image product infringement detection method may include the following steps:
It is understood that the global feature of the image in the embodiments of the present specification refers to a feature that can represent the whole image, and is used for describing the whole features such as the color and the shape of the image. The local features refer to some features which appear locally in the image, and the local features refer to some features such as edges or corners which can appear stably and have better distinguishability.
Specifically, color features, texture features and shape features in the NFT image work to be detected can be extracted as global features of the NFT image work to be detected; the edge, corner, line, curve and preset attribute region of the NFT image work to be detected can be extracted, and the local features of the NFT image work to be detected are determined. The preset attribute area is used for representing a specific area in each preset type of NFT image work, for example, a slowly changing sky in a landscape work.
Referring to the NFT image works to be detected shown in fig. 4a to 4c, it can be understood that global features of the NFT image works to be detected, such as colors of pedestrian clothes, colors of sky, shapes of traffic lights, and the like, can be extracted through fig. 4 a. Fig. 4b is an image for extracting local features such as edges and corners of the NFT image work to be detected, and further, the posture information of the pedestrian can be observed through fig. 4 b. Fig. 4c is an image obtained after the NFT image work to be detected is divided into grids, and specifically, feature extraction may be performed on some regions (for example, edge regions of clouds and regions of joints of pedestrian movement) in the grid image, and finally, a plurality of features are fused together to be used as final local features.
Specifically, the present specification may employ a deep Convolutional Neural Network (CNN) to model global context and local fine feature detection, that is, different pre-training strategies are employed to perform effective global feature pre-training on different categories of NFT image works; and further extracting refined features from the NFT image works of different categories through a circulating network. Therefore, the NFT image work to be detected can be input into the deep convolution neural network to output the similarity between the NFT image work to be detected and the image in the image database, and the comparison accuracy is improved.
And step 306, if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
Specifically, step 306 is identical to step 204, and is not described herein again.
Therefore, the embodiment in the specification can accurately calculate the similarity between the images by combining the global context information and the local fine detection characteristics of the NFT image works to be detected and the images in the image database; and repeated iteration can be performed based on a circulating structure of the deep convolutional neural network to filter the influence of noise information, so that the detection precision of the NFT image work to be detected is improved.
In the embodiment of the present specification, fig. 5 is a schematic flow chart of a method for detecting infringement of an NFT image work. As shown in fig. 5, the NFT image product infringement detection method may include the following steps:
Specifically, the hot event information is event information with a browsing volume greater than a second preset threshold. The second preset threshold is used as the preset browsing amount. For example, the second preset threshold is 10 ten thousand. Specifically, the hotspot event information may be an event that the browsing volume of each website is greater than a second preset threshold. Specifically, the data on each website can be acquired in a web crawler manner. It is known that hot spot events are typically time sensitive, and the heat of the event may decrease over time. Thus, the images in the image database may be updated at intervals. Such as but not limited to weekly updates, or monthly updates, or every three months, etc. The browsing volume of the hot spot event is the total browsing volume in the period of time.
Further, in the embodiments of the present description, ownership information of the hotspot event information may also be extracted, and the ownership information of the hotspot event information is stored in an ownership database. The ownership database can be regarded as a specific storage space and is specially used for storing ownership information of the hotspot events.
The ownership information is used to indicate author information or authoring organization information corresponding to the hotspot event information, for example, author information (such as name, contact information, communication address, and other information of an image in the hotspot news).
It can be understood that, in the embodiments of the present specification, the category, the tag, the keyword, and the like of the hotspot event can be determined by performing text classification, emotion analysis, and other technical means on the text information in the hotspot event through semantic analysis.
Specifically, the text classification of the hot events can be performed through text preprocessing, text feature extraction, and a traditional machine learning method (e.g., bayesian, support vector machine, random forest model, etc.), so as to obtain categories, tags, keywords, etc. of the hot events.
Specifically, the text information in the hot event can be preprocessed by word segmentation, word stop processing and the like based on a preset emotion dictionary method, and then character string matching is performed on the text information by using the emotion dictionary, so that positive and negative information is mined. For example, evaluation information of hot events in each dimension can be mined through emotion analysis, so that categories, labels and keywords of images can be clarified. Such as environmental protection, environmental pollution, civilian life, war, epidemic prevention and control etc.
It can be understood that the emotion dictionary can be constructed based on data sources such as micro blogs, news, forums, and learning networks, and of course, the emotion dictionary can also be trained by the existing corpora. Possibly, the description may also perform semantic analysis on the audio information and/or the video information in the hotspot event to obtain semantic information of the hotspot event.
Specifically, the description may convert video information and/or audio information included in the hotspot event into text information, and perform semantic analysis on the text information to obtain semantic information of the hotspot event, that is, information such as category, tag, and keyword of the hotspot event.
Specifically, the embodiments of the present disclosure may store the extracted information of the category, the tag, the keyword, and the like of the hot event in a semantic database, and store the image information in the hot event in an image database to form a one-to-one correspondence relationship. The semantic database can be regarded as a specific storage space and is specially used for storing semantic information of the hot spot event.
Correspondingly, the NFT image works to be detected can be screened from the NFT image work transaction platform based on the semantic information similarity, so that the range of the NFT image works to be detected is narrowed, and subsequent computing resources are reduced.
Possibly, embodiments of the present description may determine a first set of images in the NFT image work trading platform having an activity above a first preset threshold; further, determining the similarity of each image in the first image set and semantic information in a semantic database; and screening a second image set with the similarity of the semantic information in the semantic database larger than a preset semantic similarity threshold from the first image set, wherein the second image set can comprise at least one candidate image, and the NFT image work to be detected is any one candidate image in the second image set.
For example, images with liveness lower than 40% (a first preset threshold) in the NFT image work transaction platform may be filtered to obtain a first image set, and similarity between the first image set and information such as categories, labels, or keywords of each NFT image work in the semantic database is determined based on the information such as categories, labels, or keywords corresponding to each NFT image work in the first image set, so as to screen a second image set from the first image set, where similarity between the first image set and the information such as the categories, labels, or keywords of each hot event in the semantic database is greater than 60% (a preset semantic similarity threshold). In this way, detection can be performed based on the NFT image work in the second image set, so as to further reduce the range of detecting the NFT image work and reduce subsequent computing resources.
The method is not limited to the above first determining that the activity of the NFT image transaction platform is higher than the first preset threshold, then screening the second image set from the first image set, where the similarity with the semantic information in the semantic database is greater than the preset semantic similarity threshold, and then in a specific implementation, first determining that the similarity with the semantic information in the NFT image transaction platform and the semantic database is greater than the preset semantic similarity threshold, and then screening the first image set from the second image set, where the activity is higher than the first preset threshold, which is not limited in the embodiments of the present specification.
Possibly, the embodiment of the present specification may also upload the similarity between each image in the second image set and the semantic information in the semantic database to the block chain, so as to complete the fixing of the process evidence.
Specifically, step 508 is identical to step 202, and is not described herein again.
And 510, if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
Specifically, step 510 is identical to step 204, and is not described herein again.
Further, after determining that the image infringement to be detected is infringed, the NFT image work infringement detection method provided by the embodiment of the present specification may further notify a copyright owner (infringed party) to maintain the rights and interests of the copyright owner. The method comprises the following specific steps:
and step 512, determining ownership information corresponding to the image with the similarity of the NFT image work to be detected being greater than a preset similarity threshold according to the ownership database.
The ownership database in the embodiment of the present specification is used to represent a database for storing ownership information corresponding to image information in a hotspot event.
It can be understood that, under the condition that the similarity between the NFT image work to be detected and the hotspot event in the image database is greater than the preset similarity threshold, the ownership information corresponding to the hotspot event in the image database, that is, the name, contact, communication address and other information of the author, can be determined by using the ownership information stored in the ownership database.
And 514, sending a notification message to the copyright holder of the image with the similarity to the NFT image work to be detected being greater than the preset similarity threshold value based on the ownership information.
Specifically, in the embodiment of the present specification, when the similarity between the NFT image work to be detected and the hot spot event in the image database is greater than the preset similarity threshold, the ownership information of the NFT image work corresponding to the hot spot event in the image database may be sent to the copyright owner of the NFT image work to be detected in the NFT image work transaction platform, so as to remind the copyright owner that the work of the copyright owner may have an infringement problem.
Possibly, the embodiments of the present specification may also send infringement details of the NFT image work to be detected, that is, similar global image features and/or local image features, to a copyright owner of the NFT image work to be detected.
Further, the NFT image work transaction platform in this embodiment of the present specification may further prompt a copyright owner of the NFT image work to be detected that has been stored in the block chain to confirm whether the NFT image work has infringement behavior, and prompt corresponding legal risk and suggest to be off-shelf. For an NFT image work to be detected that is not uploaded to a block chain, the NFT image work transaction platform in the embodiment of the present specification may prompt a copyright owner of the NFT image work to be detected that there is an infringement risk in the NFT image work, and refuse the NFT image work to be detected to be uploaded to the NFT image work transaction platform.
Possibly, in the embodiments of the present description, ownership information corresponding to an image whose similarity to an NFT image work to be detected is greater than a preset similarity threshold may be uploaded to a block chain to complete fixing of process evidence, so as to prevent a detection result of an NFT image work transaction platform from being incorrect due to tampering of comparison information in a detection process, and increase a risk that an infringement dispute may occur in the NFT image work transaction platform.
Referring to fig. 6, in a specific example, the text information with the hot news title of "response of event party of small red book filter scene point" acquired from the web portal obtains semantic information: and searching semantic information of each NFT image work in a semantic database on the NFT image work transaction platform after words such as a small red book, a scenic spot, a three-blue house, a pink beach and the like to obtain a plurality of image sets (namely NFT image works to be detected) with the semantic similarity more than 60%. Further, an image under the hot news title can be extracted, whether an NFT image work to be detected with the similarity between the NFT image work to be detected and the image under the hot news title being greater than a similarity threshold value of 0.7 exists or not is determined based on the global features and the local features of the NFT image work to be detected in the image database, if the similarity between a plurality of NFT image works to be detected and the image under the hot news title being greater than the image similarity threshold value of 0.7 exists, author related information (ownership information) corresponding to the image under the hot news title can be further extracted, and infringement notification is sent to copyright owners of the plurality of NFT image works to be detected with infringement risks, so that infringement disputes which may occur are avoided.
Therefore, the NFT image works with high activity in the hot event and the NFT image work transaction platform can be obtained, the first image set is obtained through screening based on the semantic similarity between the image in the hot event and the NFT image works with high activity in the NFT image work transaction platform to reduce the detection range of the NFT image works to be detected, the purpose of saving computing resources is achieved, whether the NFT image works to be detected with infringement risk exist in the first image set can be determined based on the image similarity between the image in the hot event and the image works with high activity in the NFT image work transaction platform, and therefore the problems that detection results are not accurate due to image editing or transformation when NFT is directly compared can be avoided. Furthermore, related ownership information and infringement details (similar image features) can be sent to copyright owners of image works which may have infringement risks in the NFT image work transaction platform through the ownership database, and all comparison results are uploaded to a block chain, so that the risk that the NFT image work transaction platform may generate infringement disputes is reduced, and process evidences are effectively fixed.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 7 is a schematic structural diagram of an NFT image work infringement detection apparatus provided in an embodiment of the present specification. The device is used for a non-homogeneous token NFT image work transaction platform and executes the NFT image work infringement detection method of any embodiment described in the specification. As shown in fig. 7, the NFT image work infringement detection apparatus may include:
the comparison module 71 is configured to compare an NFT image work to be detected with image features of a plurality of images in an image database, so as to obtain similarity between the NFT image work to be detected and the images in the image database;
a determining module 72, configured to determine that the NFT image work to be detected infringes if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold.
The method includes the steps that image characteristics of a to-be-detected NFT image work and a plurality of images in an image database are compared, and the similarity between the to-be-detected NFT image work and the images in the image database is obtained; and if the similarity between the NFT image work to be detected and any image in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes. Therefore, on one hand, the infringement detection of the NFT product is completed by utilizing an image feature comparison mode, so that the situation that the infringement cannot be detected due to slight modification of the NFT product, such as scaling or editing of a small number of pixels, is avoided, and the accuracy of the infringement detection is improved. On the other hand, the creator is not required to provide a large amount of evidence to prove the right information of the image, a large amount of manpower and material resources are saved, tedious work such as manual examination and uploading of examination results is avoided, and the infringement detection efficiency is also improved.
In some embodiments, the comparison module 71 includes:
the extraction unit is used for extracting the global features and the local features of the NFT image works to be detected;
and the comparison unit is used for comparing the global features and the local features of the NFT image works to be detected with the global features and the local features of the images in the image database respectively to obtain the similarity between the NFT image works to be detected and the images in the image database.
In some embodiments, the extraction unit is specifically configured to:
determining the global characteristics of the NFT image works to be detected based on the color characteristics, the texture characteristics and the shape characteristics of the NFT image works to be detected;
and determining the local characteristics of the NFT image works to be detected based on the edges, corners, lines, curves and preset attribute areas of the NFT image works to be detected.
In some embodiments, the NFT image work to be detected is an image with an activity higher than a first preset threshold in the NFT image work transaction platform.
In some embodiments, the image to be detected is an image to be uploaded to an NFT transaction platform;
the device further comprises:
the to-be-detected NFT image work uploading module is used for uploading the to-be-detected NFT image work to the NFT image work transaction platform if the similarity between the to-be-detected NFT image work and any one of the images in the image database is smaller than or equal to the preset similarity threshold.
In some embodiments, the images in the image database are images in hot spot event information, and the hot spot event information is event information with a browsing volume greater than a second preset threshold.
In some embodiments, the apparatus further comprises:
the acquisition module is used for acquiring the hotspot event information;
the semantic information comparison module is used for carrying out semantic analysis on the character information in the hot event to obtain semantic information of the hot event;
the semantic information storage module is used for storing the semantic information of the hot event in a semantic database and storing the image information of the hot event in an image database;
the device further comprises:
a first image set determination module, configured to determine a first image set of the NFT image work trading platform, where the liveness of the first image set is higher than a first preset threshold;
and the screening module is used for screening a second image set from the first image set, wherein the similarity of the second image set and semantic information in the semantic database is greater than a preset semantic similarity threshold, the second image set comprises at least one candidate image, and the NFT image work to be detected is any one of the candidate images in the second image set.
In some embodiments, after the obtaining module, the apparatus further comprises:
the ownership information extraction module is used for extracting ownership information of the hotspot event information and storing the ownership information of the hotspot event information in an ownership database;
the device further comprises:
the ownership information determining module is used for determining ownership information corresponding to the image with the similarity of the NFT image works to be detected being greater than a preset similarity threshold according to the ownership database;
and the message sending module is used for sending a notification message to the copyright holder of the image with the similarity to the NFT image works to be detected being greater than a preset similarity threshold value based on the ownership information.
In some embodiments, the apparatus further comprises: and the image similarity uploading module is used for uploading the similarity between the work of the NFT image to be detected and the image in the image database, wherein the similarity between the work of the NFT image to be detected and the work of the NFT image to be detected is greater than a preset similarity threshold value, to a block chain.
In some embodiments, the apparatus further comprises:
a semantic information similarity determining module, configured to determine a semantic information similarity between each image in the first image set and the semantic database;
the device further comprises:
and the semantic information similarity uploading module is used for uploading the similarity between each image in the second image set and the semantic information in the semantic database to a block chain.
In some embodiments, the apparatus further comprises: and the ownership information uploading module is used for uploading ownership information corresponding to the image with the similarity of the NFT image work to be detected being greater than the preset similarity threshold value to the block chain.
In some embodiments, the apparatus further comprises: and the hot event semantic information comparison module is used for performing semantic analysis on the audio information and/or the video information in the hot event to obtain the semantic information of the hot event.
It should be noted that, when the NFT image infringement detection apparatus provided in the foregoing embodiment executes the NFT image infringement detection method, only the division of the functional modules is taken as an example, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the functions described above. In addition, the NFT image infringement detection apparatus provided in the above embodiment and the NFT image infringement detection method embodiment belong to the same concept, and details of an implementation process thereof are referred to in the method embodiment, and are not described herein again.
The above description of one or more embodiments is provided for illustrative purposes only and does not represent the superiority or inferiority of the embodiments.
Referring to fig. 8, a schematic structural diagram of an electronic device is provided for one or more embodiments of the present disclosure. As shown in fig. 8, the electronic device 80 may include: at least one processor 81, at least one network interface 84, a user interface 83, a memory 85, at least one communication bus 82.
Wherein a communication bus 82 is used to enable the connection communication between these components.
The user interface 83 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 83 may also include a standard wired interface and a wireless interface.
The network interface 84 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 85 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 85 includes a non-transitory computer-readable medium. The memory 85 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 805 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 85 may alternatively be at least one memory device located remotely from the processor 81. As shown in fig. 8, the memory 85, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an NFT image work infringement detection application.
In the electronic device 80 shown in fig. 8, the user interface 83 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 81 may be configured to invoke the NFT image work infringement detection application stored in the memory 85 and specifically perform the following operations:
comparing the image work of the NFT to be detected with the image characteristics of a plurality of images in an image database to obtain the similarity between the image work of the NFT to be detected and the images in the image database;
and if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
In a possible embodiment, when the processor 81 performs the comparison between the image work of the NFT to be detected and the image features of the images in the image database to obtain the similarity between the image work of the NFT to be detected and the images in the image database, the following steps are specifically performed:
extracting global features and local features of the NFT image works to be detected;
comparing the global features and the local features of the NFT image works to be detected with the global features and the local features of the images in the image database respectively to obtain the similarity between the NFT image works to be detected and the images in the image database.
In a possible embodiment, when the processor 81 performs the extraction of the global feature and the local feature of the NFT image work to be detected, specifically performing:
determining the global characteristics of the NFT image works to be detected based on the color characteristics, the texture characteristics and the shape characteristics of the NFT image works to be detected;
and determining the local characteristics of the NFT image works to be detected based on the edges, corners, lines, curves and preset attribute areas of the NFT image works to be detected.
In a possible embodiment, the NFT image works to be detected are images with liveness higher than a first preset threshold in the NFT image work transaction platform.
In a possible embodiment, the image to be detected is an image to be uploaded to an NFT transaction platform;
after the processor 81 performs the obtaining of the similarity between the NFT image work to be detected and the image in the image database, the processor further performs:
and if the similarity between the NFT image work to be detected and any one of the images in the image database is smaller than or equal to the preset similarity threshold, uploading the NFT image work to be detected to the NFT image work transaction platform.
In a possible embodiment, the image in the image database is an image in hot spot event information, and the hot spot event information is event information with a browsing volume greater than a second preset threshold.
In a possible embodiment, before performing the comparison between the image work of the NFT to be detected and the image features of the images in the image database, the processor 81 further performs:
acquiring the hotspot event information;
semantic analysis is carried out on the character information in the hot event to obtain semantic information of the hot event;
storing the semantic information of the hot event in a semantic database, and storing the image information of the hot event in an image database;
before comparing the NFT image work to be detected with the image features of the plurality of images in the image database, the processor 81 further performs:
determining a first image set with the activity higher than a first preset threshold in the NFT image work transaction platform;
and screening a second image set with the similarity of the semantic information in the semantic database larger than a preset semantic similarity threshold from the first image set, wherein the second image set comprises at least one candidate image, and the NFT image work to be detected is any one candidate image in the second image set.
In a possible embodiment, after performing the acquiring of the hotspot event information, the processor 81 further performs:
extracting ownership information of the hotspot event information, and storing the ownership information of the hotspot event information in an ownership database;
after determining the infringement of the NFT image works to be detected, the method further comprises:
determining ownership information corresponding to the image with the similarity of the NFT image works to be detected being greater than a preset similarity threshold according to the ownership database;
and sending a notification message to the copyright owner of the image with the similarity greater than a preset similarity threshold value with the NFT image work to be detected based on the ownership information.
In a possible embodiment, after performing the determining of the infringement of the NFT image work to be detected, the processor 81 further performs: uploading the similarity between the NFT image work to be detected and the image in the image database, wherein the similarity between the NFT image work to be detected and the image in the NFT image work to be detected is larger than a preset similarity threshold value, to a block chain.
In one possible embodiment, after executing the determining that the activity of the first image set in the NFT image work trading platform is higher than the first preset threshold, the processor 81 further executes, before executing the filtering from the first image set, a second image set with a similarity to the semantic information in the semantic database being higher than a preset semantic similarity threshold:
determining the similarity of each image in the first image set and semantic information in the semantic database;
after the second image set with the similarity degree with the semantic information in the semantic database larger than the preset semantic similarity threshold value is screened from the first image set, the method further comprises the following steps:
and uploading the similarity between each image in the second image set and the semantic information in the semantic database to a block chain.
In a possible embodiment, after the determining, according to the ownership database, ownership information corresponding to an image whose similarity to the NFT image work to be detected is greater than a preset similarity threshold is executed by the processor 81, the processor further executes: and uploading ownership information corresponding to the image with the similarity of the NFT image works to be detected being greater than a preset similarity threshold to a block chain.
In a possible embodiment, after performing the acquiring of the hotspot event information, the processor 81 further performs: and performing semantic analysis on the audio information and/or the video information in the hot event to obtain semantic information of the hot event.
One or more embodiments of the present specification also provide a computer-readable storage medium having stored therein instructions, which, when executed on a computer or processor, cause the computer or processor to perform one or more of the steps of the embodiments of fig. 2-3, and 5 described above. The above-mentioned NFT image infringement detection apparatus may be stored in the computer-readable storage medium if each component module is implemented in the form of a software functional unit and sold or used as an independent product.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause, in whole or in part, the processes or functions described in accordance with one or more embodiments of the present specification. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., Digital Versatile Disk (DVD)), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. And the aforementioned storage medium includes: various media capable of storing program codes, such as a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, and an optical disk. The technical features in the present examples and embodiments may be arbitrarily combined without conflict.
The above-mentioned embodiments are only described as preferred embodiments of the present disclosure, and do not limit the scope of the present disclosure, and various modifications and improvements of the technical solution of the present disclosure made by those skilled in the art without departing from the design spirit of the present disclosure should fall within the protection scope defined by the claims of the present disclosure.
Claims (15)
1. A non-homogeneous token NFT image work infringement detection method, the method comprising:
comparing the image work of the NFT to be detected with the image characteristics of a plurality of images in an image database to obtain the similarity between the image work of the NFT to be detected and the images in the image database;
and if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold, determining that the NFT image work to be detected infringes.
2. The method according to claim 1, wherein comparing the image work of the NFT to be detected with the image features of the plurality of images in the image database to obtain the similarity between the image work of the NFT to be detected and the images in the image database comprises:
extracting global features and local features of the NFT image works to be detected;
comparing the global features and the local features of the NFT image works to be detected with the global features and the local features of the images in the image database respectively to obtain the similarity between the NFT image works to be detected and the images in the image database.
3. The method according to claim 2, wherein the extracting global features and local features of the NFT image work to be detected comprises:
determining the global characteristics of the NFT image works to be detected based on the color characteristics, the texture characteristics and the shape characteristics of the NFT image works to be detected;
and determining the local characteristics of the NFT image works to be detected based on the edges, corners, lines, curves and preset attribute areas of the NFT image works to be detected.
4. The method according to claim 1, wherein the NFT image work to be detected is an image with an activity higher than a first preset threshold in a NFT image work transaction platform.
5. The method according to any one of claims 1 to 4, wherein the image in the image database is an image in hot spot event information, and the hot spot event information is event information with a browsing volume greater than a second preset threshold.
6. The method of claim 5, prior to comparing the NFT image work to be detected to image features of a plurality of images in an image database, the method further comprising:
acquiring the hotspot event information;
semantic analysis is carried out on the character information in the hot event to obtain semantic information of the hot event;
storing the semantic information of the hot event in a semantic database, and storing the image information of the hot event in an image database;
before comparing the image work of the NFT image to be detected with the image features of the images in the image database, the method further comprises:
determining a first image set with the activity higher than a first preset threshold in the NFT image work transaction platform;
and screening a second image set with the similarity of the semantic information in the semantic database larger than a preset semantic similarity threshold from the first image set, wherein the second image set comprises at least one candidate image, and the NFT image work to be detected is any one candidate image in the second image set.
7. The method of claim 6, after the obtaining the hotspot event information, the method further comprising:
extracting ownership information of the hotspot event information, and storing the ownership information of the hotspot event information in an ownership database;
after determining the infringement of the NFT image works to be detected, the method further comprises:
determining ownership information corresponding to the image with the similarity of the NFT image works to be detected being greater than a preset similarity threshold according to the ownership database;
and sending a notification message to the copyright owner of the image with the similarity greater than a preset similarity threshold value with the NFT image work to be detected based on the ownership information.
8. The method of claim 1, wherein the NFT image work to be detected is an image to be uploaded to an NFT image work trading platform;
after the similarity between the work of the NFT image to be detected and the image in the image database is obtained, the method further comprises the following steps:
and if the similarity between the NFT image work to be detected and any one of the images in the image database is smaller than or equal to the preset similarity threshold, uploading the NFT image work to be detected to an NFT image work transaction platform.
9. The method of claim 1, after determining the NFT image work to be detected infringement, the method further comprising: uploading the similarity between the NFT image work to be detected and the image in the image database, wherein the similarity between the NFT image work to be detected and the image in the NFT image work to be detected is larger than a preset similarity threshold value, to a block chain.
10. The method of claim 6, after said determining a first set of images in the NFT image work trading platform having an activity above a first preset threshold, and before said selecting a second set of images from the first set of images having a similarity to semantic information in the semantic database greater than a preset semantic similarity threshold, the method further comprising:
determining the similarity of each image in the first image set and semantic information in the semantic database;
after the second image set with the similarity degree with the semantic information in the semantic database larger than the preset semantic similarity threshold value is screened from the first image set, the method further comprises the following steps:
and uploading the similarity between each image in the second image set and the semantic information in the semantic database to a block chain.
11. The method according to claim 7, after determining, according to the ownership database, ownership information corresponding to an image whose similarity to the NFT image work to be detected is greater than a preset similarity threshold, the method further comprising: and uploading ownership information corresponding to the image with the similarity of the NFT image works to be detected being greater than a preset similarity threshold to a block chain.
12. The method of claim 6, after the obtaining the hotspot event information, the method further comprising: and performing semantic analysis on the audio information and/or the video information in the hot event to obtain semantic information of the hot event.
13. A non-homogeneous token NFT image work infringement detection device, the device comprising:
the comparison module is used for comparing the NFT image work to be detected with the image characteristics of a plurality of images in an image database to obtain the similarity between the NFT image work to be detected and the images in the image database;
the determining module is configured to determine that the NFT image work to be detected infringes if the similarity between the NFT image work to be detected and any one of the images in the image database is greater than a preset similarity threshold.
14. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-12.
15. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-12.
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