False content determination method by using artificial neural network model

Abstract

According to an embodiment of the present disclosure, disclosed is a method performed by a computing device for determining false content by using an artificial neural network model. According to the present disclosure, a computing device: acquires original data and to-be-determined data; generates original facial feature information and original body type information on the basis of inputting the original data to a first artificial neural network model; generates to-be-determined facial feature information and to-be-determined body type information on the basis of inputting the to-be-determined data to the first artificial neural network model; and determines whether the to-be-determined data is false content on the basis of the original facial feature information, the original body type information, the to-be-determined facial feature information, and the to-be-determined body type information.

Description

A method for identifying false content using an artificial neural network model

[0001] The present disclosure relates to a method for determining false content using an artificial neural network model, and more specifically, to a method for obtaining original data and data to be determined, and using an artificial neural network model to determine whether the data to be determined is false content based on original facial feature information, the original body shape information, the facial feature information to be determined, and the body shape information to be determined.

[0002] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0003] In modern society, the rapid development of the Internet and social media has enabled information to be distributed more rapidly and widely than ever before. However, amidst this information overload, the spread of misinformation and fake news has become a significant problem. In particular, misinformation can cause social unrest, lead to flawed decisions, and inflict serious harm on individuals and organizations. Existing misinformation detection methods primarily rely on rule-based systems or simple keyword matching, limiting their ability to effectively detect new types of misinformation. To address this issue, artificial neural network models have recently gained attention for identifying misinformation. Artificial neural networks possess advanced pattern recognition capabilities through large-scale data learning, enabling them to identify misinformation with greater precision and accuracy than existing methods. Therefore, there is a growing demand in the industry for technologies that utilize artificial neural network models to detect misinformation.

[0004] Korean patent registration KR 2580768 discloses a method for providing a user deepfake video analysis and monitoring service.

[0005] The present disclosure is conceived in response to the aforementioned background technology, and aims to obtain original data and discrimination target data, and determine whether the discrimination target data is false content based on original facial feature information, the original body shape information, the discrimination target facial feature information, and the discrimination target body shape information by utilizing an artificial neural network model.

[0006] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0007] According to an embodiment of the present disclosure for realizing the task as described above, a method performed in a computing device for determining false content using an artificial neural network model is disclosed. The method may include the steps of: obtaining original data and determination target data; generating original facial feature information and original body shape information based on inputting the original data into a first artificial neural network model; generating determination target facial feature information and determination target body shape information based on inputting the determination target data into the first artificial neural network model; and determining whether the determination target data is false content based on the original facial feature information, the original body shape information, the determination target facial feature information, and the determination target body shape information.

[0008] In one embodiment, the step of determining whether the data to be determined is false content based on the original facial feature information, the original body type information, the face feature information to be determined, and the body type information to be determined may include: a step of generating first similarity information based on comparing the original facial feature information and the face feature information to be determined; a step of generating second similarity information based on comparing the original body type information and the body type information to be determined; and a step of determining whether the data to be determined is false content based on the first similarity information and the second similarity information.

[0009] In one embodiment, the step of determining whether the data to be judged is false content based on the first similarity information and the second similarity information may include: if the first similarity information is equal to or greater than a predetermined first threshold value, the step of determining whether the data to be judged is false content based further on the second similarity information.

[0010] In one embodiment, when the first similarity information is equal to or greater than a predetermined first threshold, the step of determining whether the data to be judged is false content based further on the second similarity information may include: when the first similarity information is equal to or greater than a predetermined first threshold and the second similarity information is equal to or greater than a predetermined second threshold, the step of determining that the data to be judged is not false content.

[0011] In one embodiment, the step of determining whether the data to be judged is false content based on the first similarity information and the second similarity information may include: if the second similarity information is less than a predetermined second threshold value, the step of determining whether the data to be judged is false content based further on the first similarity information.

[0012] In one embodiment, when the second similarity information is less than a predetermined second threshold, the step of determining whether the data to be judged is false content based further on the first similarity information may include: when the second similarity information is less than a predetermined second threshold and the first similarity information is less than a predetermined first threshold, the step of determining that the data to be judged is false content.

[0013] In one embodiment, the second threshold value may be a value dynamically determined based on the first similarity information.

[0014] In one embodiment, the step of obtaining original data and target data for discrimination may further include the step of performing upscaling based on inputting the original data and the target data for discrimination into a second artificial neural network model.

[0015] According to an embodiment of the present disclosure for realizing the above-described task, a computer program stored in a computer-readable storage medium is disclosed that causes a computing device to perform operations for determining false content using an artificial neural network model. The operations may include: an operation of acquiring original data and data to be determined; an operation of generating original facial feature information and original body shape information based on inputting the original data into a first artificial neural network model; an operation of generating target facial feature information and target body shape information based on inputting the target data into the first artificial neural network model; and an operation of determining whether the target data is false content based on the original facial feature information, the original body shape information, the target facial feature information, and the target body shape information.

[0016] According to an embodiment of the present disclosure for realizing the above-described task, a computing device for determining false content using an artificial neural network model is disclosed. The computing device includes one or more processors and a memory, and the one or more processors may be configured to obtain original data and determination target data, generate original facial feature information and original body shape information based on inputting the original data into a first artificial neural network model, generate determination target facial feature information and determination target body shape information based on inputting the determination target data into the first artificial neural network model, and determine whether the determination target data is false content based on the original facial feature information, the original body shape information, the determination target facial feature information, and the determination target body shape information.

[0017] This disclosure utilizes not only facial information but also body type information, and by adjusting the threshold for determining false information based on body type information, it enables more accurate detection of false content. By considering both body type and facial information together, false content can be identified more accurately, reducing social confusion and preventing harm to individuals and organizations. Consequently, this disclosure significantly increases the accuracy and efficiency of false information detection.

[0018] Meanwhile, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents described below.

[0019] FIG. 1 is a block diagram of a computing device for determining false content using an artificial neural network model according to one embodiment of the present disclosure.

[0020] FIG. 2 is a schematic diagram illustrating a network function according to one embodiment of the present disclosure.

[0021] FIG. 3 is a conceptual diagram illustrating a process for determining false content using an artificial neural network model according to one embodiment of the present disclosure.

[0022] FIG. 4 is a flowchart illustrating a process for determining false content using an artificial neural network model according to one embodiment of the present disclosure.

[0023] FIG. 5 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0024] The present disclosure discloses a method for obtaining original data and discrimination target data, and using an artificial neural network model to determine whether the discrimination target data is false content based on original facial feature information, the original body shape information, the discrimination target facial feature information, and the discrimination target body shape information.

[0025] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0026] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. One or more components of the present disclosure may be distributed between two or more computers, i.e., execution environments. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted via signals to another system over a network such as the Internet).

[0027] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0028] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0029] And, the term “at least one of A or B” should be interpreted to mean “if it includes only A”, “if it includes only B”, or “if it is combined in the composition of A and B”.

[0030] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0031] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments set forth herein. The present disclosure is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0032] In this disclosure, "fake content" may refer to content in which the original content has been altered by modifying the original image or video using an artificial neural network model or other means without the permission of the authorized party. For example, fake content may include deepfake content, i.e., fake video or audio generated using a deep learning model.

[0033] FIG. 1 is a block diagram of a computing device for performing tasks on an object using an artificial neural network model according to one embodiment of the present disclosure.

[0034] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0035] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0036] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network using backpropagation.

[0037] At least one of the CPU, GPGPU, and TPU of the processor (110) can process network function learning. For example, the CPU and GPGPU can jointly process network function learning and data classification using the network function. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be used together to process network function learning and data classification using the network function. Furthermore, a computer program executed in a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0038]

[0039] In one embodiment of the present disclosure, the processor (110) may obtain original data and target data. In the present disclosure, "original data" may include an image or video containing a human face or body region. Furthermore, in the present disclosure, "target data" may refer to data suspected of being modified from the original data. The "target data" may be determined to be "false data" or "not false data" through the false content determination method of the present disclosure.

[0040] The processor (110) may perform upscaling based on inputting the original data and the discrimination target data into the second artificial neural network model after acquiring the original data and the discrimination target data. If the resolution of the data is excessively low, there is a possibility that the artificial neural network may not be able to properly extract the features of the data, so the processor (110) may perform upscaling on the basis of inputting the original data and the discrimination target data into the second artificial neural network model.

[0041] The processor (110) can generate original facial feature information and original body shape information based on inputting original data into the first artificial neural network model.

[0042] For example, the processor (110) may extract a face portion and a body portion of the original data through image segmentation based on inputting the original data into the first artificial neural network model. Thereafter, the processor (110) may extract a feature vector including information related to features of the face portion from the face portion of the original data, and may extract a feature vector including information related to features of the body portion from the body portion of the original data. At this time, the feature vector may include vectorized information about invariant, unique features of an individual's face, skeleton, or body shape.

[0043] The processor (110) can generate target facial feature information and target body shape information based on inputting target data to be determined into the first artificial neural network model. The process of generating target facial feature information and target body shape information from the target data can be performed in the same manner as the process of generating original facial feature information and original body shape information from the original data described above.

[0044] The processor (110) can determine whether the data to be determined is false content based on the original facial feature information, the original body shape information, the face feature information to be determined, and the body shape information to be determined. The specific process by which the processor (110) determines whether the data to be determined is false content will be described later with reference to FIG. 4. In the process of determining whether the data to be determined is false content, the processor (110) can auxiliaryly utilize the similarity between body shape information, focusing on the similarity between facial feature information.

[0045] This disclosure utilizes an artificial neural network model to determine whether content is falsified, utilizing not only facial features but also body shape information from the original data. This increases the accuracy of the determination results. Therefore, this disclosure significantly increases the efficiency of determining falsified content.

[0046]

[0047] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0048] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0049] In addition, the network unit (150) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0050] In the present disclosure, the network unit (150) can use any type of wired or wireless communication system.

[0051] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0052]

[0053] FIG. 2 is a schematic diagram illustrating a network function according to one embodiment of the present disclosure.

[0054] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be composed of a set of interconnected computational units, generally referred to as nodes. These nodes may also be referred to as neurons. A neural network comprises at least one node. The nodes (or neurons) comprising a neural network may be interconnected by one or more links.

[0055] Within a neural network, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, meaning that any node that is in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0056] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by the user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set on the link corresponding to each input node.

[0057] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0058] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0059] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0060] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

[0061] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using DNNs, one can identify latent structures in data. For example, one can identify the latent structures of images, text, videos, audio, and music (e.g., what objects are in the image, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). DNNs can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siamese networks, and generative adversarial networks (GANs). The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

[0062] In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the dimensionality after preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure in which the number of nodes decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and decoder) may be kept above a certain number (e.g., more than half of the input layer), as too few nodes may not transmit enough information.

[0063] Neural networks can learn using at least one of the following methods: supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Neural network learning can be the process of applying knowledge to the neural network to perform a specific action.

[0064] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. Supervised learning uses training data with the correct answer labeled for each training data (i.e., labeled training data). Unsupervised learning, on the other hand, may not have the correct answer labeled for each training data. For example, in the case of supervised learning for data classification, the training data may be data with each category labeled. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the training data labels. Alternatively, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of neural network training to quickly achieve a certain level of performance, thereby increasing efficiency. A lower learning rate can be used in the later stages of training to increase accuracy.

[0065] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout, which disables some nodes in the network during the learning process, and the use of batch normalization layers.

[0066]

[0067] FIG. 3 is a conceptual diagram illustrating a process for determining false content using an artificial neural network model according to one embodiment of the present disclosure.

[0068] The processor (110) inputs original content (310) into a first neural network model (330), and the first neural network model can output original facial feature information and original body shape information in the form of a feature vector (340).

[0069] Likewise, the processor (110) inputs the content to be judged (320) into the first neural network model (330), and the first neural network model (330) can output the facial feature information to be judged and the body shape information to be judged in the form of a feature vector (350).

[0070] Thereafter, the processor (110) can generate a similarity judgment result based on inputting the feature vector (340) extracted from the original content (310) and the feature vector (350) extracted from the content to be judged (320) into the similarity judgment module (360). Thereafter, the processor (110) can finally determine whether the content to be judged (320) is false content based on inputting the similarity judgment result into the judgment module (370).

[0071]

[0072] FIG. 4 is a flowchart illustrating a process for determining false content using an artificial neural network model according to one embodiment of the present disclosure.

[0073] The process for determining false content using the artificial neural network model of the present disclosure may include a step of obtaining original data and data to be determined (S410), a step of generating original facial feature information and original body shape information based on inputting the original data into the first artificial neural network model (S420), a step of generating facial feature information and body shape information to be determined based on inputting the data to be determined into the first artificial neural network model (S430), a step of generating first similarity information based on comparing the original facial feature information and the face feature information to be determined (S440), a step of generating second similarity information based on comparing the original body shape information and the body shape information to be determined (S450), and a step of determining whether the data to be determined is false content based on the first similarity information and the second similarity information (S460).

[0074] In step S410, the processor (110) can obtain original data and target data for determination. The specific process by which the processor obtains data has been described above with reference to FIG. 1.

[0075] In step S420, the processor (110) can generate original facial feature information and original body shape information based on inputting original data into the first artificial neural network model.

[0076] In step S430, the processor (110) can generate target facial feature information and target body shape information based on inputting target data to be determined into the first artificial neural network model. It has been described above with reference to FIG. 3 that the processor can generate original facial feature information, original body shape information, target facial feature information, and target body shape information based on the target data and original data.

[0077] In step S440, the processor (110) may generate first similarity information based on a comparison of the original facial feature information and the target facial feature information. For example, the processor (110) may calculate vector similarity between the original facial feature information in vector form and the target facial feature information, and may express the similarity as a real number value between 0 and 1.

[0078] In step S450, the processor (110) may generate second similarity information based on a comparison of the original body shape information and the body shape information to be determined. For example, the processor (110) may calculate vector similarity between the original body shape information and the body shape information to be determined in vector form, and express the similarity as a real number value between 0 and 1.

[0079] In step S460, the processor (110) can determine whether the target data for determination is false content based on the first similarity information and the second similarity information.

[0080] In one embodiment, the processor (110) may determine whether the target data is false content based on the second similarity information if the first similarity information is equal to or greater than a first predetermined threshold. Specifically, the processor (110) may determine that the target data is not false content if the first similarity information is equal to or greater than a first predetermined threshold and the second similarity information is equal to or greater than a second predetermined threshold. That is, even if the facial features are similar to each other, the processor (110) may further examine whether the body features are similar to a certain level or more, and if the body features are similar to a certain level or more, the processor may determine that the target data is not false content.

[0081] In another embodiment, the processor (110) may determine whether the target data is false content based more on the first similarity information if the second similarity information is less than a second predetermined threshold. Specifically, the processor (110) may determine that the target data is false content if the second similarity information is less than the second predetermined threshold and the first similarity information is less than the first threshold. That is, even if the body features are not similar to each other, the processor (110) may further examine whether the facial features are not more than a certain degree dissimilar, and if even the facial features are not similar, determine that the target data is false content.

[0082] In the present disclosure, the second threshold value can be dynamically determined based on the first similarity information. For example, the first threshold value can be set to increase in proportion to the first similarity information. That is, the higher the similarity between the original data and the target data, the higher the similarity criterion for the body shape features that serve as auxiliary judgment criteria can be set, thereby enabling more accurate judgment of false content based on similarity.

[0083]

[0084] Meanwhile, a computer-readable medium storing a data structure according to an embodiment of the present disclosure is disclosed.

[0085] A data structure can refer to the organization, management, and storage of data to enable efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include connections between user-defined data elements. Physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0086] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one piece of data is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each piece having a pointer. In a linked list, a pointer can contain information about the next or previous piece of data. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0087] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structures can include tree data structures. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0088] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they are collectively referred to as neural networks. The data structure may include a neural network. And the data structure including the neural network may be stored on a computer-readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. The data structure including the neural network may include any of the components disclosed above. That is, the data structure including the neural network may be configured to include all or any combination of preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. In addition to the aforementioned configurations, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0089] The data structure may include data input to a neural network. The data structure including the data input to the neural network may be stored on a computer-readable medium. The data input to the neural network may include training data input during the neural network training process and / or input data input to the neural network after training has been completed. The data input to the neural network may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to the neural network. Accordingly, the data structure may include data that is the target of preprocessing and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0090] The data structure may include weights of the neural network. (In this specification, the terms "weight" and "parameter" may be used interchangeably.) The data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set for links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0091] By way of example and not limitation, the weights may include weights that vary during the neural network training process and / or weights that have completed neural network training. The weights that vary during the neural network training process may include weights at the start of the training cycle and / or weights that vary during the training cycle. The weights that have completed neural network training may include weights that have completed the training cycle. Accordingly, a data structure including the weights of a neural network may include a data structure including weights that vary during the neural network training process and / or weights that have completed neural network training. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of a neural network. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0092] A data structure including neural network weights can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The serialized data structure including neural network weights can be reconstructed on the same or different computing devices through deserialization. The data structure including neural network weights is not limited to serialization. Furthermore, the data structure including neural network weights can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0093] The data structure may include hyperparameters of a neural network. Furthermore, the data structure including the hyperparameters of the neural network may be stored on a computer-readable medium. The hyperparameters may be variables that can be varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle repetitions, weight initialization (e.g., setting a range of weight values ​​to be subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0094]

[0095] FIG. 5 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0096] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may also be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0097] Generally, program modules include routines, programs, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0098] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0099] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0100] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0101] An exemplary environment for implementing various aspects of the present disclosure is illustrated, including a computer (1102), which includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0102] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0103] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0104] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those of ordinary skill in the art will appreciate that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0105] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0106] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0107] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0108] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0109] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0110] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0111] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0112] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0113] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0114] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0115] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0116] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0117] As described above, the relevant contents have been described in the best form for carrying out the invention.

Claims

1. A method performed on a computing device to determine false content using an artificial neural network model, A step of acquiring original data and target data for determination; A step of generating original facial feature information and original body shape information based on inputting the original data into a first artificial neural network model; A step of generating discrimination target facial feature information and discrimination target body shape information based on inputting the discrimination target data into the first artificial neural network model; and A step of determining whether the judgment target data is false content based on the original facial feature information, the original body type information, the judgment target facial feature information, and the judgment target body type information; including, method.

2. In paragraph 1, The step of determining whether the data to be judged is false content based on the original facial feature information, the original body type information, the face feature information to be judged, and the body type information to be judged is: A step of generating first similarity information based on comparing the original facial feature information and the target facial feature information; A step of generating second similarity information based on comparing the original body shape information and the body shape information to be determined; and A step of determining whether the target data for determination is false content based on the first similarity information and the second similarity information; including, method.

3. In paragraph 2, Based on the first similarity information and the second similarity information, the step of determining whether the target data is false content is: If the first similarity information is greater than or equal to a predetermined first threshold value, a step of determining whether the target data is false content based further on the second similarity information; including, method.

4. In paragraph 3, If the first similarity information is greater than or equal to a predetermined first threshold value, the step of determining whether the target data is false content based on the second similarity information is: A step of determining that the target data is not false content when the first similarity information is greater than or equal to a predetermined first threshold value and the second similarity information is greater than or equal to a predetermined second threshold value; including, method.

5. In paragraph 2, Based on the first similarity information and the second similarity information, the step of determining whether the target data is false content is: If the second similarity information is less than a predetermined second threshold value, a step of determining whether the target data is false content based further on the first similarity information; including, method.

6. In paragraph 5, If the second similarity information is less than a predetermined second threshold value, the step of determining whether the target data is false content based on the first similarity information is: A step of determining that the judgment target data is false content when the second similarity information is less than a predetermined second threshold and the first similarity information is less than a predetermined first threshold; including, method.

7. In paragraph 4 or paragraph 6, The second threshold value is a value dynamically determined based on the first similarity information; method.

8. In paragraph 1, Steps to obtain source data and target data are: A step of performing upscaling based on inputting the above original data and the above judgment target data into a second artificial neural network model; including more, method.

9. A computer program stored in a computer-readable storage medium that causes a computing device to perform operations for determining false content using an artificial neural network model, the operations comprising: The action of obtaining the original data and the target data to be determined; An operation of generating original facial feature information and original body shape information based on inputting the original data into a first artificial neural network model; An operation of generating discrimination target facial feature information and discrimination target body shape information based on inputting the discrimination target data into the first artificial neural network model; and An operation of determining whether the judgment target data is false content based on the original facial feature information, the original body type information, the judgment target facial feature information, and the judgment target body type information; including, A computer program stored on a computer-readable storage medium.

10. A computing device for determining false content using an artificial neural network model. one or more processors; and memory; Including, One or more of the above processors, Obtain the original data and the target data, Based on inputting the original data into the first artificial neural network model, original facial feature information and original body shape information are generated, Based on inputting the above discrimination target data into the above first artificial neural network model, discrimination target facial feature information and discrimination target body shape information are generated, and Based on the original facial feature information, the original body type information, the face feature information to be determined, and the body type information to be determined, it is configured to determine whether the data to be determined is false content. Computing device.

Images