Video template infringement detection method, system, device and medium

Abstract

The present application discloses a method, apparatus, device and storage medium for detecting video template infringement, which includes obtaining a target video template to be detected and a reference video template; performing anti-interference graph synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy; extracting a first material track of the first video template and extracting a second material track of the second video template; determining correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track; and determining an infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies. This method can achieve automated detection of video template infringement, and has better detection efficiency and accuracy. The present application can be widely applied in the field of video technology.

Description

Technical Field

[0001] This application relates to the field of video technology, and in particular to a method, apparatus, device, and storage medium for detecting video template infringement. Background Art

[0002] Currently, with the development of information technology, more and more enterprise system applications have been developed to provide services for users. For example, with the rapid development of multimedia technology, the creation and application of video templates have become increasingly widespread. Users can create, share, and use relevant video templates, thus improving the efficiency and quality of video generation.

[0003] In practical applications, it is found that there are cases of stealing video templates in many applications. In order to protect the rights and interests of original authors, it is necessary to detect infringement of video templates. Currently, video template infringement detection methods mainly rely on manual comparison and identification, which are not only inefficient but also easily affected by subjective factors, resulting in inaccurate detection results. In addition, with the continuous increase in the number of video templates, the manual detection method can no longer meet the actual needs.

[0004] In summary, the problems existing in the related technologies need to be solved urgently. Summary of the Invention

[0005] An object of this application is to solve at least to some extent one of the technical problems existing in the related technologies.

[0006] To this end, an object of an embodiment of this application is to provide a method, apparatus, device, and storage medium for detecting video template infringement.

[0007] To achieve the above technical object, the technical solutions adopted in the embodiments of this application include:

[0008] On the one hand, an embodiment of this application provides a method for detecting video template infringement, and the method includes:

[0009] Obtain a target video template to be detected and a reference video template for comparison with the target video template;

[0010] Perform anti-interference graph synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy; wherein, the first video template is the target video template obtained after the anti-interference graph synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference graph synthesis analysis processing;

[0011] Extract a first material trajectory of the first video template and extract a second material trajectory of the second video template;

[0012] Determine the correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track;

[0013] Determine the infringement coefficient of the target video template relative to the benchmark video template according to the correlation data of the first video template and the second video template under all strategies.

[0014] In addition, according to the video template infringement detection method of the above embodiments of the present application, the following additional technical features may also be included:

[0015] Further, in an embodiment of the present application, the strategy types of the anti-interference map synthesis analysis processing include complete transparent synthesis analysis processing, complete unsaturated synthesis analysis processing, complete saturated synthesis analysis processing, and random synthesis analysis processing.

[0016] Further, in an embodiment of the present application, performing anti-interference map synthesis analysis processing on the target video template and the benchmark video template under multiple strategies to obtain the corresponding first video template and second video template under each strategy includes:

[0017] Taking the target video template as the first video template corresponding to the target video template under the strategy of complete transparent synthesis analysis processing, and taking the benchmark video template as the second video template corresponding to the benchmark video template under the strategy of complete transparent synthesis analysis processing;

[0018] Inputting the same black background picture to the target video template and the benchmark video template to obtain the first video template corresponding to the target video template under the strategy of complete unsaturated synthesis analysis processing, and the second video template corresponding to the benchmark video template under the strategy of complete unsaturated synthesis analysis processing;

[0019] Inputting the same white background picture to the target video template and the benchmark video template to obtain the first video template corresponding to the target video template under the strategy of complete saturated synthesis analysis processing, and the second video template corresponding to the benchmark video template under the strategy of complete saturated synthesis analysis processing;

[0020] Inputting the same random color picture to the target video template and the benchmark video template to obtain the first video template corresponding to the target video template under the strategy of random synthesis analysis processing, and the second video template corresponding to the benchmark video template under the strategy of random synthesis analysis processing.

[0021] Further, in an embodiment of the present application, extracting the first material track of the first video template includes:

[0022] Extract key frames from the first video template to obtain a key frame sequence corresponding to the first video template;

[0023] Input the key frame sequence into a pre-trained material trajectory model to extract a first material trajectory of the first video template;

[0024] Wherein, the first material trajectory includes several key frames in the first video template.

[0025] Further, in an embodiment of the present application, the determining the correlation data between the first video template and the second video template under the corresponding strategy according to the first material trajectory and the second material trajectory includes:

[0026] Extract a first trajectory feature of the first material trajectory and a second trajectory feature of the second material trajectory; wherein, the first trajectory feature includes several first key frame features, and the second trajectory feature includes the same number of second key frame features as the number of the first key frame features;

[0027] Calculate the Euclidean distance between each group of the first key frame features and the second key frame features;

[0028] Determine the similarity between each group of the first key frame features and the second key frame features according to the Euclidean distance;

[0029] Calculate the difference between 1 and the similarity to determine the dissimilarity between each group of the first key frame features and the second key frame features;

[0030] Calculate the product of the dissimilarities corresponding to each group of the first key frame features and the second key frame features to obtain a first value;

[0031] Determine the correlation data between the first video template and the second video template according to the difference between 1 and the first value.

[0032] Further, in an embodiment of the present application, the determining the infringement coefficient of the target video template relative to the reference video template according to the correlation data between the first video template and the second video template under all strategies includes:

[0033] Detect a first ratio of the number of key frames in the first material trajectory to the total number of key frames in the first video template;

[0034] Detect a second ratio of the number of key frames in the second material trajectory to the total number of key frames in the second video template;

[0035] Determine the correlation influence value of the first video template and the second video template according to the first ratio and the second ratio;

[0036] Calculate the average value of the correlation influence values corresponding to all strategies to obtain the infringement coefficient of the target video template relative to the reference video template.

[0037] Further, in an embodiment of the present application, the determining the correlation influence value of the first video template and the second video template according to the first ratio and the second ratio includes:

[0038] Calculate the correlation influence value of the first video template and the second video template through the following formula:

[0039] R = (1 - (1 - R1)(1 - R2))C

[0040] In the formula, R represents the correlation influence value of the first video template and the second video template, R1 represents the first ratio, R2 represents the second ratio, and C represents the correlation data of the first video template and the second video template.

[0041] On the other hand, an embodiment of the present application provides a video template infringement detection device, and the device includes:

[0042] An acquisition unit, configured to acquire a target video template to be detected and a reference video template for comparison with the target video template;

[0043] A processing unit, configured to perform anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template for each strategy; wherein, the first video template is the target video template obtained after the anti-interference map synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference map synthesis analysis processing;

[0044] An extraction unit, configured to extract a first material track of the first video template and extract a second material track of the second video template;

[0045] A calculation unit, configured to determine the correlation data of the first video template and the second video template corresponding to the corresponding strategy according to the first material track and the second material track;

[0046] A statistics unit, configured to determine the infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies.

[0047] On the other hand, an embodiment of the present application provides an electronic device, including:

[0048] At least one processor;

[0049] At least one memory for storing at least one program;

[0050] When the at least one program is executed by the at least one processor, the at least one processor is caused to implement the above-mentioned video template infringement detection method.

[0051] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to implement the above-mentioned video template infringement detection method when executed by the processor.

[0052] The advantages and beneficial effects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present application:

[0053] The video template infringement detection method, device, equipment and storage medium disclosed in the embodiments of the present application obtain a target video template to be detected and a reference video template for comparison with the target video template; perform anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy; wherein, the first video template is the target video template obtained after the anti-interference map synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference map synthesis analysis processing; extract a first material trajectory of the first video template and extract a second material trajectory of the second video template; determine correlation data of the first video template and the second video template under the corresponding strategy according to the first material trajectory and the second material trajectory; determine an infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies. This method can realize automated video template infringement detection, and the detection efficiency and accuracy are better, which is beneficial to reducing labor costs and improving the effectiveness of copyright protection of video templates. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the accompanying drawings of the relevant technical solutions in the embodiments of the present application or the prior art. It should be understood that the accompanying drawings in the following introduction are only for conveniently and clearly expressing some embodiments of the technical solutions in the present application, and those skilled in the art can also obtain other accompanying drawings according to these drawings without creative efforts.

[0055] Figure 1 Schematic diagram of the implementation environment of a video template infringement detection method provided in an embodiment of the present application;

[0056] Figure 2 Schematic flow diagram of a video template infringement detection method provided in an embodiment of the present application;

[0057] Figure 3 Schematic diagram of the strategy type for anti-interference map synthesis analysis and processing provided in an embodiment of the present application;

[0058] Figure 4 Schematic diagram of a random color picture provided in an embodiment of the present application;

[0059] Figure 5 Schematic diagram of the structure of a video template infringement detection device provided in an embodiment of the present application;

[0060] Figure 6 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Detailed implementation manners

[0061] The present application will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments. The described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0062] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0064] 1) Video template: A pre-designed video element and effect that allows users to modify and edit it according to personal needs to produce various types of videos.

[0065] 2) Material: A certain dynamic or static design element.

[0066] 3) Trajectory: The material moves or changes along a specific path.

[0067] At present, with the development of information technology, more and more enterprise system applications are being developed to provide services for users. For example, with the rapid development of multimedia technology, the creation and application of video templates are becoming more and more extensive. Users can create, share and use relevant video templates, thereby improving the efficiency and quality of video generation.

[0068] In actual applications, it is found that many applications have plagiarized video templates. In order to protect the rights of original authors, it is necessary to detect the infringement of video templates. At present, the video template infringement detection method mainly relies on manual comparison and recognition, which is not only inefficient, but also easily affected by subjective factors, resulting in inaccurate detection results. In addition, with the continuous increase in the number of video templates, manual detection methods can no longer meet actual needs.

[0069] In view of this, a video template infringement detection method is provided in an embodiment of the present application, which obtains a target video template to be detected and a benchmark video template for comparison with the target video template; performs anti-interference graph synthesis analysis processing under multiple strategies on the target video template and the benchmark video template to obtain the first video template and the second video template corresponding to each strategy; wherein the first video template is the target video template obtained after the anti-interference graph synthesis analysis processing, and the second video template is the benchmark video template obtained after the anti-interference graph synthesis analysis processing; extracts the first material track of the first video template, and extracts the second material track of the second video template; according to the first material track and the second material track, determines the correlation data of the first video template and the second video template under the corresponding strategy; according to the correlation data of the first video template and the second video template under all strategies, determines the infringement coefficient of the target video template relative to the benchmark video template. This method can realize automated video template infringement detection, and the detection efficiency and accuracy are better, which is conducive to reducing labor costs and improving the effectiveness of copyright protection of video templates.

[0070] Please refer to Figure 1 , Figure 1 The schematic diagram of the implementation environment of a video template infringement detection method provided in the embodiment of the present application is shown. In the implementation environment, the main hardware and software entities involved include a terminal device 110 and a backend server 120. The terminal device 110 and the backend server 120 are connected in communication.

[0071] Specifically, the video template infringement detection method provided in the embodiments of the present application can be executed alone on the terminal device 110 side, or can be executed alone on the background server 120 side, or can be executed based on data interaction between the terminal device 110 and the background server 120.

[0072] Among them, the terminal device 110 in the above embodiments may include a mobile phone, a computer, a smart wearable device, a PDA device, a smart voice interaction device, a smart home appliance, a vehicle-mounted terminal, etc., but is not limited thereto. The background server 120 may be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0073] A communication connection may be established between the terminal device 110 and the background server 120 through a wireless network or a wired network. The wireless network or the wired network uses standard communication technologies and / or protocols. The network may be set to the Internet or any other network, such as including but not limited to any combination of a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or a virtual private network.

[0074] Of course, it can be understood that Figure 1 the implementation environment in Figure 1 is only some optional application scenarios of the video template infringement detection method provided in the embodiments of the present application. The actual application is not fixed to the

[0075] shown software and hardware environment.

[0076] Next, in combination with the introduction of the foregoing implementation environment, a video template infringement detection method provided in the embodiments of the present application will be introduced and described.

[0076] Please refer to Figure 2 Figure 2 which is a schematic diagram of a video template infringement detection method provided in the embodiments of the present application. The video template infringement detection method includes but is not limited to:

[0077] Step 210: Obtain a target video template to be detected and a reference video template for comparison with the target video template;

[0078] Step 220: Perform anti-interference graph synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a first video template and a second video template corresponding to each strategy; wherein, the first video template is the target video template obtained after the anti-interference graph synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference graph synthesis analysis processing;

[0079] Step 230: Extract the first material track of the first video template and the second material track of the second video template;

[0080] Step 240: Determine the correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track;

[0081] Step 250: Determine the infringement coefficient of the target video template relative to the benchmark video template according to the correlation data of the first video template and the second video template under all strategies.

[0082] In the embodiments of the present application, a method for detecting video template infringement is provided. This method can effectively improve two main technical problems in the field of video template copyright protection: one is that the traditional infringement detection method is inefficient and difficult to meet the rapid detection requirements of large-scale video content; the other is that the detection accuracy is low, and false positives or false negatives are likely to occur, affecting the effectiveness of copyright protection.

[0083] Specifically, in the embodiments of the present application, when detecting video template infringement, a basic resource database can be established. Various video templates can be stored in this database. When a new video template appears, the existing video templates in the resource database can be used to detect its infringement to determine whether the new video template may be infringing. In the embodiments of the present application, the video template to be detected for infringement can be recorded as the target video template, and the video template compared with the target video template can be recorded as the benchmark video template.

[0084] It is easy to understand that when detecting the target video template, multiple benchmark video templates can be used to determine whether the target video template is infringing. In the embodiments of the present application, the process of comparing the target video template with one benchmark video template is introduced, which can be reused for multiple other benchmark video templates to determine the infringement coefficients of the target video template and each benchmark video template. The number of benchmark video templates specifically participating in the comparison is not limited in this application.

[0085] In the embodiments of the present application, when detecting the target video template, it and the benchmark video template for comparison can be obtained. Both the target video template and the benchmark video template can be a video clip, which includes several image frames. Then, anti-interference map synthesis analysis and processing can be performed on the target video template and the benchmark video template under multiple strategies. Under each strategy, the processed result of the target video template can be obtained, recorded as the first video template, and the processed result of the benchmark video template can be obtained, recorded as the second video template.

[0086] In the embodiments of the present application, anti-interference map synthesis analysis processing can achieve the elimination of interference caused by the superposition of multiple layers in a video template. Generally speaking, layer blending modes include main modes such as normal, dissolve, darken, lighten, saturation, difference, color, etc. and their extended modes. According to the principle of the blending algorithm, the normal and dissolve modes are affected by the layer transparency, and the other modes are affected by the layer color (BGR). When stealing a video template to generate other video templates, people often simply process its layers to avoid possible infringement detection. Therefore, in the embodiments of the present application, anti-interference map synthesis analysis processing is performed on the target video template and the reference video template to reduce the impact of multi-layer blending on detection.

[0087] Exemplarily, for example, referring to Figure 3 , Figure 3 shows a schematic diagram of the strategy types of an anti-interference map synthesis analysis processing provided in the embodiments of the present application. The strategy types of the anti-interference map synthesis analysis processing in the embodiments of the present application may include fully transparent synthesis analysis processing, fully unsaturated synthesis analysis processing, fully saturated synthesis analysis processing, and random synthesis analysis processing. These strategies will be introduced and described separately below.

[0088] Under the strategy of fully transparent synthesis analysis processing, the target video template itself can be used as the first video template, and the reference video template itself can be used as the second video template.

[0089] Under the strategy of fully unsaturated synthesis analysis processing, by using the user layer, the same black background (b:0, g:0, r:0, a:255) picture can be input into each video template to remove the color information in the image, making the detection focus more on aspects such as the brightness and contrast of the image, improving the recognition of special effect elements composed of brightness changes rather than color changes, thereby reducing the interference of color mixing on the analysis. In other words, the same black background picture can be input into the target video template and the reference video template to obtain the first video template and the second video template.

[0090] Under the strategy of fully saturated synthesis analysis processing, by using the user layer, a white background (b:255, g:255, r:255, a:255) picture can be input into the video template to emphasize or change the color saturation of the image. By enhancing the color saturation, the special effects become more prominent, and to a certain extent, the process of special effect analysis is simplified. In other words, the same white background picture can be input into the target video template and the reference video template to obtain the first video template and the second video template.

[0091] Under the strategy of random synthesis analysis processing, the same random color (random color range for each pixel point in the picture: b:0→255, g:0→255, r:0→255, a:0→255) picture is input into the video template. Referring toFigure 4 , Figure 4 shows a schematic diagram of a random color picture provided in an embodiment of the present application. A random color picture, that is, a random pixel color picture, can be used as a "noise" source, which can effectively disperse and weaken interference factors in other layers. When such a picture is mixed with a target image, its irregular color distribution will break the original interference pattern, making the special effect analysis more accurate and reliable. At this time, the same random color picture is input into the target video template and the reference video template to obtain a first video template and a second video template.

[0092] After obtaining the first video template and the second video template corresponding to each strategy, the material track of the first video template can be extracted, and the material track of the second video template can be extracted. The material track of the first video template is denoted as the first material track, and the material track of the second video template is denoted as the second material track.

[0093] Exemplarily, taking the extraction process of the first material track as an example, it may include:

[0094] Performing key frame extraction on the first video template to obtain a key frame sequence corresponding to the first video template;

[0095] Inputting the key frame sequence into a pre-trained material track model to extract the first material track of the first video template;

[0096] Wherein, the first material track includes several key frames in the first video template.

[0097] In the embodiment of the present application, for the first video template and the second video template, key frames can be extracted from them respectively to obtain corresponding key frame sequences. Then, a pre-trained material track model can be used to extract the key frame sequences to obtain the first material track of the first video template and the second material track of the second video template. Here, the material track can be a new sequence composed of key frames containing specific materials in the original key frame sequence. Regarding the specific parameters and structures of the material track model, the present application makes no limitation. For example, in some embodiments, the material track model can be a sequence-to-sequence model, with the input being the key frame sequence and the output being the material track.

[0098] In the embodiment of the present application, after obtaining the first material track and the second material track, the correlation data between the first video template and the second video template can be determined under the corresponding strategy. Here, the correlation data can be used to characterize the similarity degree between the first video template and the second video template.

[0099] Specifically, in some embodiments, determining the correlation data between the first video template and the second video template under the corresponding policy according to the first material trajectory and the second material trajectory includes:

[0100] Extracting the first trajectory feature of the first material trajectory and extracting the second trajectory feature of the second material trajectory; wherein, the first trajectory feature includes a number of first key frame features, and the second trajectory feature includes the same number of second key frame features as the number of the first key frame features;

[0101] Calculating the Euclidean distance between each group of the first key frame features and the second key frame features;

[0102] Determining the similarity between each group of the first key frame features and the second key frame features according to the Euclidean distance;

[0103] Calculating the difference between 1 and the similarity to determine the dissimilarity between each group of the first key frame features and the second key frame features;

[0104] Calculating the product of the dissimilarities corresponding to each group of the first key frame features and the second key frame features to obtain a first value;

[0105] Determining the correlation data between the first video template and the second video template according to the difference between 1 and the first value.

[0106] In the embodiments of the present application, when determining the correlation data between the first video template and the second video template, for the first material trajectory and the second material trajectory, feature extraction can be performed on them. For example, image features of each key frame of the material trajectory can be extracted through a convolutional layer and a pooling layer. The features extracted from the first material trajectory are denoted as first key frame features, and the features extracted from the second material trajectory are denoted as second key frame features. The quantities of both can be the same and are grouped in pairs. Then, for each group of the first key frame features and the second key frame features, their Euclidean distance can be calculated. For example, it can be a standardized Euclidean distance, and the present application does not limit this. In the embodiments of the present application, according to the Euclidean distance, the similarity between each group of the first key frame features and the second key frame features can be determined. Since the smaller the Euclidean distance, the greater the similarity, a feasible formula for calculating the similarity can be expressed as: w = 1 / (1 + d), where w represents the similarity and d represents the Euclidean distance.

[0107] Then, calculate the difference between 1 and the similarity, and determine the dissimilarity between the first key-frame features and the second key-frame features in each group. The dissimilarity can be expressed as 1 - w. Then, in the embodiments of the present application, the product of the dissimilarities corresponding to the first key-frame features and the second key-frame features in each group can be calculated to obtain a first value L. Calculate the difference between 1 and the first value, and the correlation data between the first material track and the second material track can be determined, so that the correlation data between the first video template and the second video template can be further determined.

[0108] It should be noted that in the embodiments of the present application, the number of material tracks in the video template may be multiple. When there are multiple material tracks, the correlation data corresponding to each group of material tracks can be summed to obtain the correlation data between the first video template and the second video template.

[0109] In the embodiments of the present application, according to the correlation data between the first video template and the second video template under all strategies, the infringement coefficient of the target video template relative to the reference video template can be determined. Specifically, this process may include:

[0110] Detect a first ratio of the number of key frames in the first material track to the total number of key frames in the first video template;

[0111] Detect a second ratio of the number of key frames in the second material track to the total number of key frames in the second video template;

[0112] Determine the correlation influence value between the first video template and the second video template according to the first ratio and the second ratio;

[0113] Calculate the average value of the correlation influence values corresponding to all strategies to obtain the infringement coefficient of the target video template relative to the reference video template.

[0114] In the embodiments of the present application, when calculating the infringement coefficient, a first ratio of the number of key frames in the first material track to the total number of key frames in the first video template can be detected, denoted as R1, and a second ratio of the number of key frames in the second material track to the total number of key frames in the second video template can be detected, denoted as R2. According to the first ratio and the second ratio, the correlation influence value of this group of track correlation data on the overall can be determined. For example, it can be calculated by the following formula:

[0115] R = (1 - (1 - R1)(1 - R2))C

[0116] In the formula, R represents the correlation influence value between the first video template and the second video template, R1 represents the first ratio, R2 represents the second ratio, and C represents the correlation data between the first video template and the second video template.

[0117] Through the above process, the correlation influence value corresponding to the video template under different strategies can be determined, and the average value thereof can be obtained to get the infringement coefficient of the target video template relative to the benchmark video template.

[0118] In the embodiments of the present application, the higher the infringement coefficient, the more likely it is that the target video template is consistent with the benchmark video template, and the higher the probability of infringement. Specifically, a threshold can be set for comparison. If the infringement coefficient of the target video template relative to the benchmark video template exceeds the threshold, it indicates that the target video template is infringing, and the object of infringement is the current benchmark video template.

[0119] The technical solutions in the present application have at least the following technical effects:

[0120] 1) Significantly improve the detection efficiency: The deep learning model can automatically extract high-level feature representations from video templates, avoiding the cumbersome process of manual feature design, and at the same time supporting parallel processing of a large amount of video content, thus greatly improving the efficiency of infringement detection.

[0121] 2) Improve the detection accuracy: Utilizing the powerful learning ability of the deep learning model, the present application can capture the subtle differences and unique features in video templates, and through fine feature comparison and similarity calculation, achieve accurate identification of infringement behaviors, reducing the probability of misjudgment and missed judgment.

[0122] 3) Enhance the copyright protection ability: By constructing and maintaining a video template feature database, the present application provides an efficient and reliable infringement detection tool for copyright holders, helping to discover and stop infringement behaviors in a timely manner, safeguarding the legitimate rights and interests of video template creators, and promoting the healthy and orderly development of video content.

[0123] 4) Strong adaptability: The systems and methods of the present application are not only applicable to the existing video template infringement detection, but also can adapt to the future development and changes of video content forms and technologies by continuously optimizing the deep learning model and feature database, maintaining its long-term effectiveness and competitiveness.

[0124] In summary, the present application effectively solves the problems of efficiency and accuracy in video template infringement detection by introducing deep learning technology, provides strong technical support for the copyright protection of video templates, and has significant technical effects and broad application prospects.

[0125] Referring to Figure 5 , an apparatus for detecting video template infringement is further provided in the embodiments of the present application, including:

[0126] An acquisition unit 510, configured to acquire a target video template to be detected and a benchmark video template for comparison with the target video template;

[0127] A processing unit 520, configured to perform anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies, and obtain a corresponding first video template and a second video template under each strategy; wherein, the first video template is the target video template obtained after the anti-interference map synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference map synthesis analysis processing;

[0128] An extraction unit 530, configured to extract a first material track of the first video template and a second material track of the second video template;

[0129] A calculation unit 540, configured to determine correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track;

[0130] A statistics unit 550, configured to determine an infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies.

[0131] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented by the device embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0132] Referring to Figure 6 , an electronic device is provided in an embodiment of the present application, including:

[0133] At least one processor 610;

[0134] At least one memory 620, configured to store at least one program;

[0135] When at least one program is executed by at least one processor 610, at least one processor 610 is caused to implement the above video template infringement detection method.

[0136] Similarly, the content in the above method embodiments is applicable to the electronic device embodiments of the present application. The functions specifically implemented by the electronic device embodiments of the present application are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0137] An embodiment of the present application further provides a computer-readable storage medium, in which a program executable by a processor 610 is stored, and the program executable by the processor 610 is used to execute the above video template infringement detection method when executed by the processor 610.

[0138] Similarly, the content in the above method embodiments is applicable to the embodiments of this computer-readable storage medium. The functions specifically implemented in the embodiments of this computer-readable storage medium are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0139] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of this application are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated where the order of various operations is changed and where sub-operations described as part of a larger operation are executed independently.

[0140] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding this application. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skill of an engineer. Thus, those skilled in the art can implement this application as set forth in the claims without undue experimentation using ordinary skill. It is also understood that the specific concepts disclosed are illustrative only and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0141] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0142] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0143] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0144] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0145] In the above description of this specification, the description with reference to terms such as "one embodiment / Example", "another embodiment / Example", or "certain embodiments / Examples" etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0146] Although embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application. The scope of the present application is defined by the claims and their equivalents.

[0147] The above has specifically described the preferred embodiments of the present application, but the present application is not limited to the embodiments. Those skilled in the art can make various equivalent deformations or substitutions without violating the spirit of the present application, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present application.

Claims

1. A method for detecting video template infringement, characterized in that The method includes: Obtaining a target video template to be detected and a reference video template for comparison with the target video template; Performing anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy; wherein, the first video template is the target video template obtained after the anti-interference map synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference map synthesis analysis processing; Extracting a first material track of the first video template and a second material track of the second video template; Determining correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track; Determining an infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies.

2. The video template infringement detection method according to claim 1, characterized in that The strategy types of the anti-interference map synthesis analysis processing include full transparency synthesis analysis processing, full unsaturation synthesis analysis processing, full saturation synthesis analysis processing, and random synthesis analysis processing.

3. The video template infringement detection method according to claim 2, characterized in that, The performing anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy includes: Using the target video template as the first video template corresponding to the target video template under the strategy of full transparency synthesis analysis processing, and using the reference video template as the second video template corresponding to the reference video template under the strategy of full transparency synthesis analysis processing; Inputting the same black background picture to the target video template and the reference video template to obtain the first video template corresponding to the target video template under the strategy of full unsaturation synthesis analysis processing, and the second video template corresponding to the reference video template under the strategy of full unsaturation synthesis analysis processing; Inputting the same white background picture to the target video template and the reference video template to obtain the first video template corresponding to the target video template under the strategy of full saturation synthesis analysis processing, and the second video template corresponding to the reference video template under the strategy of full saturation synthesis analysis processing; Inputting the same random color picture to the target video template and the reference video template to obtain the first video template corresponding to the target video template under the strategy of random synthesis analysis processing, and the second video template corresponding to the reference video template under the strategy of random synthesis analysis processing.

4. The video template infringement detection method according to claim 1, wherein The extracting the first material track of the first video template includes: Performing key frame extraction on the first video template to obtain a key frame sequence corresponding to the first video template; Inputting the key frame sequence into a pre-trained material track model to extract the first material track of the first video template; Wherein, the first material track includes several key frames in the first video template.

5. A video template infringement detection method according to claim 1, characterized in that, Determining the correlation data of the first video template and the second video template under the corresponding strategy according to the first material track and the second material track includes: Extracting the first track feature of the first material track and extracting the second track feature of the second material track; wherein, the first track feature includes a plurality of first key frame features, and the second track feature includes the same number of second key frame features as the number of the first key frame features; Calculating the Euclidean distance between each group of the first key frame features and the second key frame features; Determining the similarity between each group of the first key frame features and the second key frame features according to the Euclidean distance; Calculating the difference between 1 and the similarity to determine the dissimilarity between each group of the first key frame features and the second key frame features; Calculating the product of the dissimilarities corresponding to each group of the first key frame features and the second key frame features to obtain a first value; Determining the correlation data of the first video template and the second video template according to the difference between 1 and the first value.

6. The video template infringement detection method according to claim 1, wherein Determining the infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all strategies includes: Detecting a first ratio of the number of key frames in the first material track to the total number of key frames in the first video template; Detecting a second ratio of the number of key frames in the second material track to the total number of key frames in the second video template; Determining the correlation influence value of the first video template and the second video template according to the first ratio and the second ratio; Calculating the average value of the corresponding correlation influence values under all strategies to obtain the infringement coefficient of the target video template relative to the reference video template.

7. A video template infringement detection method according to claim 6, characterized in that, Determining the correlation influence value of the first video template and the second video template according to the first ratio and the second ratio includes: Calculating the correlation influence value of the first video template and the second video template through the following formula: R = (1 - (1 - R1)(1 - R2))C In the formula, R represents the correlation influence value of the first video template and the second video template, R1 represents the first ratio, R2 represents the second ratio, and C represents the correlation data of the first video template and the second video template.

8. A video template infringement detection device, characterized in that The device includes: An acquisition unit, configured to acquire a target video template to be detected and a reference video template for comparison with the target video template; A processing unit, configured to perform anti-interference map synthesis analysis processing on the target video template and the reference video template under multiple strategies to obtain a corresponding first video template and a second video template under each strategy; wherein, the first video template is the target video template obtained after the anti-interference map synthesis analysis processing, and the second video template is the reference video template obtained after the anti-interference map synthesis analysis processing; An extraction unit, configured to extract the first material track of the first video template and extract the second material track of the second video template; A calculation unit, configured to determine correlation data of the first video template and the second video template under a corresponding policy according to the first material track and the second material track; A statistics unit, configured to determine an infringement coefficient of the target video template relative to the reference video template according to the correlation data of the first video template and the second video template under all policies.

9. An electronic device, characterized in that, Comprising: At least one processor; At least one memory, configured to store at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a video template infringement detection method according to any one of claims 1-7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to implement a video template infringement detection method according to any one of claims 1-7.

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