KR102516548B1 - System and method for preventing forgery of video based on blockchain and computer program for the same - Google Patents

Abstract

A blockchain-based image forgery prevention system includes an input unit configured to receive image information from a photographing device; an image division unit configured to divide the image information into a plurality of unit image data; a hash extractor configured to calculate a hash value of each of the plurality of unit image data; and recording a hash value of each of the plurality of unit image data in data blocks on a blockchain network, wherein each of the data blocks includes a hash value of a preceding data block and a hash value of the unit image data, and a storage unit configured to record a hash value of unit image data preceding the unit image data corresponding to each data block among the unit image data in a data block preceding each data block. When using the video storage system, video data such as video files are divided by a predetermined unit time (eg, frame), and the hash value for each segment is stored in a data block on a blockchain network in the form of a chain by time. By storing, forgery or manipulation of image information can be prevented.

Description

Blockchain-based image forgery prevention system and method and computer program for it

Embodiments relate to a blockchain-based video forgery prevention system and method, and a computer program therefor. More specifically, the embodiments relate to a technology for preventing forgery or manipulation of stored images by applying block chain technology to storage of image information in a device that shoots and stores images such as a black box or CCTV.

In companies with a lot of information that needs to be kept secure, such as trade secrets, a control system is installed at the door to control the access of outsiders to the facility or to manage the access of users, and it is possible to check the person entering the facility through a video. CCTV is often installed. In addition, CCTVs are installed for the purpose of crime prevention in an enclosed space such as an elevator or a space where a lot of people come and go.

For example, Patent Publication No. 10-2019-0135634 discloses a real-time crime monitoring elevator capable of monitoring incidents occurring inside the elevator by transmitting images of CCTVs installed inside the elevator to monitors installed for each floor.

However, images stored in video recording devices such as CCTVs or black boxes can be easily stolen or occupied by a malicious third party, so there is a possibility that the images can be forged or manipulated, and the original images can be easily damaged. there is a problem with Because of these limitations, even when the video data corresponds to major evidence for a crime or an incident, there is a problem in that the authenticity of the video cannot be trusted due to the possibility of forgery or manipulation by others, and thus the value as evidence is reduced.

Publication No. 10-2019-0135634

According to one aspect of the present invention, by applying a blockchain-based technology to storage of image information in devices such as black boxes and CCTVs that shoot and store images, it is possible to check forgery or manipulation of images in advance, It is possible to provide a blockchain-based image forgery prevention system and method that can enhance the reliability of stored images so that the image data can be used as evidence, and a computer program for this.

An image forgery prevention system based on blockchain according to an embodiment includes an input unit configured to receive image information from a photographing device; an image division unit configured to divide the image information into a plurality of unit image data; a hash extractor configured to calculate a hash value of each of the plurality of unit image data; and recording a hash value of each of the plurality of unit image data in data blocks on a blockchain network, wherein each of the data blocks includes a hash value of a preceding data block and a hash value of the unit image data, and a storage unit configured to record a hash value of unit image data preceding the unit image data corresponding to each data block among the unit image data in a data block preceding each data block.

A blockchain-based image forgery prevention system according to an embodiment uses images included in the image information as input values for a machine learning-based analysis model to detect objects corresponding to the images. It further includes a configured machine learning unit. At this time, the image division unit specifies an image in which one or more of the type of object detected by the machine learning unit and the number of detected objects is changed, and converts the front and back of the specified image into different unit image data from the image information. It is further configured to divide.

In one embodiment, the storage unit stores transaction information for a token on the blockchain network defined corresponding to the unit image data to data blocks on the blockchain network together with the unit image data. It is further configured to record.

In one embodiment, the storage unit stores token input/output details or the transaction information defined by the transaction information based on the type or number of objects detected by the machine learning unit for an image included in the unit image data. is further configured to determine one or more of the quantities of tokens defined by

A blockchain-based image forgery prevention system according to an embodiment includes pre-registered wallet address information for each user and further includes a user management unit configured to receive user information corresponding to at least a part of the image information. At this time, the storage unit is further configured to determine a wallet address for the unit image data corresponding to each data block based on the user information, and to define the transaction information using the determined wallet address as a sender or recipient.

In one embodiment, the storage unit is further configured to store the image information in the image storage system or an external storage.

A method for preventing forgery and falsification of an image based on a block chain according to an embodiment includes receiving image information from a photographing device by a block chain-based image forgery and falsification prevention system; Dividing the image information into a plurality of unit image data by the blockchain-based image forgery prevention system; calculating, by the blockchain-based image forgery prevention system, a hash value of each of the plurality of unit image data; and recording, by the blockchain-based image forgery prevention system, a hash value of each of the plurality of unit image data in data blocks on a blockchain network. In this case, each of the data blocks includes a hash value of a preceding data block and a hash value of the unit image data. In addition, in the step of recording data blocks on the blockchain network, the block chain-based image forgery prevention system precedes the unit image data corresponding to each data block among the plurality of unit image data. and recording the hash value of the image data in a data block preceding each data block.

In the block chain-based image forgery prevention method according to an embodiment, before the step of dividing into a plurality of unit image data, the block chain-based image forgery prevention system performs machine learning-based processing of images included in the image information. The method further includes detecting objects corresponding to the images by using them as input values to the analysis model. At this time, in the step of dividing into a plurality of unit image data, the block chain-based image forgery prevention system changes at least one of the type of object detected by the machine learning-based analysis model and the number of detected objects. specifying the image to be; and dividing, by the blockchain-based image forgery prevention system, the front and back of the specified image in the image information into different unit image data.

According to an exemplary embodiment, the block chain-based image forgery prevention system transmits transaction information for a token on the block chain network defined corresponding to the unit image data to the unit image data. Further comprising the step of writing to the data blocks on the blockchain network with.

In one embodiment, the step of recording the transaction information in data blocks on the blockchain network may include the machine learning-based analysis of the image included in the unit image data by the blockchain-based image forgery prevention system. Based on the type or number of objects detected by the model, determining at least one of a token input/output history defined by the transaction information or a quantity of tokens defined by the transaction information.

A block chain-based image forgery prevention method according to an embodiment includes storing wallet address information for each user pre-registered in the block chain-based image forgery prevention system; and receiving, by the blockchain-based image forgery prevention system, user information corresponding to at least a portion of the image information. At this time, the step of recording the transaction information in the data blocks on the blockchain network, the blockchain-based image forgery prevention system determines the unit image data corresponding to each data block based on the user information. determining the wallet address; and defining, by the blockchain-based video forgery prevention system, the transaction information using the determined wallet address as a sender or receiver.

The block chain-based image forgery prevention method according to an embodiment further includes, by the block chain-based image forgery prevention system, storing the image information in the image storage system or external storage.

A computer program according to an embodiment is combined with hardware to execute the block chain-based image forgery prevention method according to the above-described embodiments, and may be stored in a computer-readable medium.

Using a blockchain-based image forgery prevention system and method according to an aspect of the present invention, image data such as a video file is divided by a predetermined unit time (eg, frame) to hash Values can be created, and these hash values can be stored in the form of hourly chains in data blocks on the blockchain network. According to the video storage system and method, when there is forgery or manipulation of video data, the hash value of the video data becomes different from the hash value previously stored on the blockchain, so it can be recognized that the video has been manipulated. . As a result, forgery and alteration of image data can be prevented, and reliability can be increased when images are used as evidence for illegal acts such as crimes and incidents.

1 is a schematic block diagram of a block chain (blockchain) based video forgery prevention system according to an embodiment.
2 is a flowchart illustrating each step of a method for preventing forgery and alteration of an image based on a block chain according to an embodiment.
3 is a conceptual diagram of a process of segmenting unit image data by applying object detection based on machine learning in a block chain-based image forgery prevention method according to an embodiment.
4 is a conceptual diagram of a process of storing a hash value of image data in a data block on a blockchain in a method for preventing forgery and alteration of an image based on a blockchain according to an embodiment.
5 is a conceptual diagram for explaining monitoring of an image through a transaction of a blockchain-based token in a method for preventing forgery and alteration of an image based on a blockchain according to an embodiment.

Hereinafter, with reference to the drawings, look at the embodiments of the present invention in detail.

1 is a schematic block diagram of a block chain (blockchain) based video forgery prevention system according to an embodiment.

Referring to FIG. 1 , the block chain-based image forgery prevention system 3 according to the present embodiment receives image information from one or more photographing devices 1 and blocks information necessary to prevent forgery of the received image information. It serves to store in the distributed ledger on the chain network (2). For example, the photographing device 1 may be a black box or CCTV, but is not limited thereto. Also, in one embodiment, the blockchain-based image forgery prevention system 3 may store the image information itself in the internal storage of the blockchain-based image forgery prevention system 3 or the storage of the external server 4.

For the above operation, the blockchain-based image forgery prevention system 3 is configured to be communicatively connected to the blockchain network 2 and / or external server 4 through wired and / or wireless networks. Also, in one embodiment, the blockchain-based image forgery prevention system 3 may receive image information from one or more photographing devices 1 in a communication method through a network. In this specification, the communication method between the blockchain-based video forgery prevention system 3 and the photographing device 1, the blockchain network 2 and / or the external server 4 is an object through a wired and / or wireless network. It can include all communication methods that can be networked and objects, and is not limited to wired communication, wireless communication, 3G, 4G, or other methods.

For example, the blockchain-based image forgery prevention system (3) is LAN (Local Area Network), MAN (Metropolitan Area Network), GSM (Global System for Mobile Network), EDGE (Enhanced Data GSM Environment), HSDPA (High Speed Downlink Packet Access), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi ), Voice over Internet Protocol (VoIP), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), By one or more communication methods selected from the group consisting of Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and ultrasound-enabled communication. It may be configured to communicate, but is not limited thereto.

The blockchain network 2 includes a plurality of nodes 200, and each node 200 is communicatively connected to each other through a peer-to-peer network, The generated transaction is shared, and the node 200 determined in the mining competition by the consensus algorithm creates a data block to be recorded in the distributed ledger and stores transaction information in the generated block. The chain of created data blocks is shared by all nodes 200 of the blockchain network 2, and the nodes 200 synchronize the chain of blocks.

At this time, the blockchain-based image forgery prevention system 3 according to the embodiments divides the image information received from the photographing device 1 into predetermined units to generate image data, and converts the hash value of each image data into blocks. By storing it in a data block on the chain network (2), it functions to prevent forgery and alteration of image data.

In one embodiment, the blockchain-based video forgery prevention system 3 includes an input unit 31, an image segmentation unit 32, a hash extraction unit 34 and a storage unit 36. In addition, in one embodiment, the block chain-based image forgery prevention system 3 further includes a machine learning unit 33 for intelligently dividing image information. In addition, in one embodiment, the block chain-based image forgery prevention system 3 further includes a storage 35 for storing the image information itself in the block chain-based image forgery prevention system 3. Furthermore, in one embodiment, the blockchain-based video forgery prevention system 3 further includes a user management unit 37 for reflecting the user in the information recorded in the blockchain network 2.

The block chain-based video forgery prevention system 3 and each unit 31 to 37 included therein according to embodiments may have an aspect of being entirely hardware, or partially hardware and partially software. For example, the blockchain-based video forgery prevention system 3 and each unit 31 to 37 included in it provide hardware and related software for processing data of a specific format and content or/or exchanging data in an electronic communication method. can be called In this specification, terms such as "unit", "module", "device", "terminal", "server" or "system" are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device including a CPU or other processor. Also, software driven by hardware may refer to a running process, an object, an executable file, a thread of execution, a program, and the like.

In addition, each element constituting the blockchain-based image forgery prevention system 3 is not necessarily intended to refer to a separate device that is physically separated from each other. That is, each unit 31 to 37 of the blockchain-based image forgery prevention system 3 shown in FIG. 1 operates the hardware constituting the blockchain-based image forgery prevention system 3 to operate It is only functionally classified according to, and each part does not necessarily have to be provided independently of each other. Of course, depending on embodiments, one or more of the above-described parts 31 to 37 may be implemented as separate devices that are physically separated from each other.

The input unit 31 may be directly connected to one or more photographing devices 1 or connected in a communication method through a wired and/or wireless network to receive image information from the input unit 31 . In the embodiments of the present specification, the block chain-based image forgery prevention system 3 is shown as a separate block from the photographing device 1, but according to the embodiment, the block chain-based image forgery prevention system 3 It may be implemented as a part of the photographing device 1 located in the device 1 . In addition, the number of photographing devices 1 shown in the drawing is merely illustrative and does not limit the number of photographing devices 1 operating in relation to the blockchain-based image forgery prevention system 3 .

The image divider 32 may divide the image information received from the photographing device 1 through the input unit 31 into a plurality of unit image data. For example, in one embodiment, unit image data may refer to each frame of an image in image information having a form such as a video file, but is not limited thereto, and includes a plurality of preset frames or frames. Unit image data may be defined using image information corresponding to a preset unit time.

In one embodiment, the image segmentation unit 32 may determine a partition to divide unit image data based on a result of detecting an object included in image information by the machine learning unit 33 . For example, the machine learning unit 33 may detect an object included in the image by using images included in the image information as input values for a machine learning-based analysis model. At this time, the image segmentation unit 32 specifies an image in which the type and/or number of objects detected by the machine learning unit 33 is changed among a series of images constituting the image information as a boundary image, and the specified boundary image The front and back images may be divided into different unit image data. In this case, each unit image data may have different lengths.

The hash extractor 34 may extract a hash value by applying a predetermined hash algorithm to each unit image data generated through the above process. In addition, the storage unit 36 may store the hash value of each unit image data extracted by the hash extraction unit 34 in a data block on the blockchain network 2 . In the blockchain network 2, data blocks are sequentially created, and each data block stores the hash value of the previous data block. At this time, the storage unit 36 may store the hash value of each unit image data consecutive in time series in successive data blocks on the blockchain.

As a result, the data blocks of the blockchain each contain the hash value of the unit image data together with the hash value of the preceding data block, and the hash value of the unit image data successive in time series is stored in each successive data block. do. That is, when there are consecutive first and second data blocks and there are consecutive first and second image data, a hash value of the first unit image data may be stored in the first data block, and the second image data may be stored in the first data block. The hash value of the first data block together with the hash value of the second unit image data may be stored in the data block. In such a structure in which data blocks store the hash value of the previous block, if some data block on the blockchain is tampered with, the hash value to be recorded in the subsequent data blocks will all change due to the nature of the hash function, It is possible to determine whether image information has been manipulated through a change in hash value.

The storage unit 36 may include a communication unit 361 for storing a hash value generated using a hash function in a data block on the blockchain network 2. Also, the communication unit 361 may store unit image data or image information itself in the storage of the external server 4 through a wired and/or wireless network. Alternatively, in one embodiment, the storage unit 36 may be configured to store unit image data or image information itself in the storage 35 of the blockchain-based image forgery prevention system 3 .

In one embodiment, the storage unit 36 may further include a transaction generator 362 that generates transaction information to be recorded in a data block on a blockchain together with a hash value of unit image data. The transaction generating unit 362 may record transaction information about a token whose input/output details are recorded in a data block in a distributed ledger defined by data blocks on a blockchain. In this case, the movement of the token defined by the transaction information may be determined based on the characteristics of unit image data whose hash value is recorded in the corresponding data block. For example, the transaction information may have the form of a smart contract, but is not limited thereto.

In one embodiment, the transaction generation unit 362 may define a token transaction according to the type and/or number of objects detected from the image by the machine learning unit 33 . Alternatively, in one embodiment, the block chain-based image forgery prevention system 3 receives user information related to image information through the user management unit 37, and the transaction generator 362 corresponds to unit image data. Transactions can also be defined to reflect user information (e.g., wallet address) to the movement of tokens. The form of the transaction reflecting the object or user information detected from the image will be described later in detail.

In one embodiment, the storage unit 36 records the image information itself in the internal storage of the external server 4 or the blockchain-based image forgery prevention system 3, but it is possible to specify the storage location of the image information in this storage. Possible location information may be further recorded in a data block on the blockchain network (2).

In addition, in one embodiment, the storage unit 36 records the hash value generated from each unit video data of the video information in a data block on the blockchain network 2, but the video information itself is the main unit of the blockchain network 2. It can be uploaded to data blocks on the sidechain, not the main net. When uploading video information to a sidechain, the introduction of a sidechain that uses a consensus algorithm such as Proof of Authority (POA) has the advantage of significantly increasing the transaction processing speed and lowering the fee burden. .

2 is a flowchart illustrating each step of a method for preventing forgery and alteration of an image based on a block chain according to an embodiment. The block chain-based image forgery prevention method according to embodiments may be performed using the block chain-based image forgery prevention system according to embodiments of the present invention. Hereinafter, for convenience of description, each step of a method for preventing forgery and alteration of an image based on a blockchain according to embodiments will be described with reference to FIGS. 1 and 2 .

Referring to FIGS. 1 and 2 , the input unit 31 of the blockchain-based image forgery prevention system 3 may receive image information from one or more photographing devices 1 (S1). As described above, the image divider 32 may divide image information into a plurality of unit image data, and each unit image data may have a preset number of frames or a preset time period.

Meanwhile, in one embodiment, the image segmentation unit 32 may segment unit image data based on object recognition in an image by the machine learning unit 33 . In this embodiment, the machine learning unit 33 may recognize an object in an image by using each image constituting image information as an input value for a machine learning-based analysis model (S2). At this time, the machine learning-based analysis model is learned through a learning image in which the type of object is known in advance, and functions to detect an object in an unknown image using parameters based on the learning result, for example, A convolutional neural network (CNN) may be used, but is not limited thereto.

At this time, the image segmentation unit 32 specifies a boundary image in which the type and/or number of objects detected from the image by the machine learning unit 33 change significantly beyond a preset level (S3), and uses the boundary image Image information may be divided into unit image data so that the front and back of the boundary image become separate unit image data (S4).

3 is a conceptual diagram of a process of segmenting unit image data by applying object detection based on machine learning in a block chain-based image forgery prevention method according to an embodiment.

Referring to FIG. 3 , image information 100 in the form of a moving picture may be composed of a series of images 111-115 and 121-125 sequentially successive to each other. At this time, the blockchain-based video forgery prevention system may determine the type of object included in each image, the area in the image where the object is located, the number of objects included in the image, etc. through machine learning-based analysis. For example, image 115 represents an image including an object 1150 corresponding to an adult male, and image 121 includes an object 1210 corresponding to an adult female and an object 1211 corresponding to a child. represents an image.

At this time, the image 115 and the image 121 are temporally successive images, but as shown in the drawing, the object 1150 included in the preceding image 115 and the object included in the following image 121 The types and numbers of (1210, 1211) have changed. In actual shooting images, there will be many cases in which the detected object changes more gradually, but for convenience of description, an extreme form in which the type and number of objects change in one image interval is exemplified, making it easy for those skilled in the art. It will be understood.

In the example of FIG. 3 , since the image 115 and/or the image 121 in which the type and number of objects change greatly corresponds to the boundary image, the unit image data 11 and 12 may be defined using this as a reference point for division. For example, the section from the beginning of the video information 100 to the image 115 becomes the first unit image data 11, and the section starting from the next image 121 until another boundary image is detected is the first unit image data 11. Image information 100 may be divided into a plurality of unit image data to form 2 unit image data 12 .

In the example shown in FIG. 3, it has been described as an example that the types (adult male, adult female, child) and number of objects included in the image of each unit image data are all different, but depending on the embodiment, the number of objects is the same. It is also possible to specify as a boundary image images in which the type of object is different, or the number of objects is different even if the type of object is the same.

Referring back to FIGS. 1 and 2 , as described above, the block chain-based video forgery prevention system 3 for each unit image data partitioned based on the number of frames or the length of time or based on object detection The hash extractor 34 may calculate a hash value by applying a hash function (S5). As a hash function for calculating the hash value, for example, Secure Hash Algorithm (SHA) may be used, but is not limited thereto.

Next, the storage unit 36 of the blockchain-based image forgery prevention system 3 may record the hash value of unit image data in a data block on the blockchain network 2 (S8). At this time, the storage unit 36 of the blockchain-based image forgery prevention system 3 generates transaction information corresponding to each unit image data (S7), and transmits the transaction information along with the hash value of the unit image data to the blockchain network. (2) It is also possible to write to the above data block (S8).

4 is a conceptual diagram of a process of storing a hash value of image data in a data block on a blockchain in a method for preventing forgery and alteration of an image based on a blockchain according to an embodiment.

Referring to FIG. 4, a blockchain may be composed of a series of data blocks 301-303 consecutive to each other, and each data block 301-303 has counter information representing the serial number of the corresponding data block in the distributed ledger. (3013). In addition, hash values (3011, 3021, 3031) of unit image data given to each data block (301-303) are recorded, and hash values (3012, 3022, 3032) of preceding data blocks on the blockchain are recorded. this is recorded For example, each of the data blocks 301 to 303 stores hash values 3011, 3021, and 3031 of consecutive unit image data, and the preceding data block (eg, 302) For example, a hash value 3022 of 301 is stored.

Due to the nature of the hash function, if any part of the data is changed, the hash value is changed to a completely different value, so if the hash value 3011 of the image data included in the preceding block 301 is changed due to the modulation of the image information, the corresponding data The hash value of block 301 is changed to a completely different value. In addition, as a result, since the previous block hash value 3022 of the data block 302 following the corresponding data block 301 is changed, the hash value of the following data block 302 is also changed and the blockchain is chained in this way. The hash values of all blocks after the block corresponding to the video data in which modulation has occurred in . Therefore, by storing the hash value of the image data in the blockchain, it is possible to detect that the image is manipulated or forged.

In each of the data blocks 301 to 303, transaction information 3014, 3024, and 3034 related to the movement of tokens whose issuance and deposit and withdrawal are managed by the distributed ledger on the blockchain may be further recorded. In an embodiment, transaction information 3014, 3024, and 3034 may be defined by reflecting characteristics of unit image data corresponding to image data hash values 3011, 3021, and 3031 of each data block 301 to 303. . For example, according to the characteristics of the image data, the movement direction, sender and receiver, and movement amount related to the movement of the token defined by the transaction information 3014, 3024, and 3034 may be determined, which will be described in detail later.

Referring back to FIGS. 1 and 2 , in one embodiment, the storage unit 36 of the blockchain-based video forgery prevention system 3 stores the hash value of unit video data on the blockchain, while storing the same image information. may be received from different photographing devices 1, and blocks in which hash values of unit image data generated from the image information received from each photographing device 1 are recorded may be cross-verified (S6). To this end, each photographing device 1 may be configured to communicate with each other through a wired and / or wireless network, and the blockchain-based image forgery prevention system 3 may be any one or both photographing devices ( 1), or may be implemented as a separate device capable of communicating with all of them.

An image captured by one of the photographing devices 1 is input to the blockchain-based image forgery prevention system 3 provided in or interlocked with the corresponding photographing device 1, while another photograph is taken through communication through a network. It can be delivered to device 1. In another photographing device 1, image information is processed through a blockchain-based image forgery prevention system 3 provided or interlocked therewith. In this case, a hash value of unit image data and a data block are respectively generated based on the image information of the two photographing devices 1. When there is no manipulation or forgery of the image information, the generated hash value of a series of data blocks The value should be the same on both instruments. Therefore, if the hash values of the data blocks are cross-verified, if there is a difference in the hash values of the data blocks generated in both devices, it can be known that data manipulation exists.

In addition, in one embodiment, transaction information is recorded in a data block along with a hash value of unit image data, but transaction information is defined by reflecting the characteristics of unit image data, so that video information can be monitored, managed, or monitored through transaction information. can make it possible. At this time, the characteristics of the unit image data include the type and/or number of objects detected based on machine learning from the images constituting the unit image data, and information capable of specifying a user associated with the corresponding image information together with image information. In this case, it may refer to user information corresponding to unit image data.

For example, the transaction generation unit 362 of the blockchain-based image forgery prevention system 3 defines a transaction corresponding to each unit image data of image information as a movement of tokens. It may be determined based on the presence, type, and/or number of objects identified from the image included in the unit image data. For example, a transaction for moving a token from a preset sending address to a preset receiving address corresponding to each data block is defined, but when the number of objects detected from an image of unit image data is 1, token movement by 1 occurs. and, when the number of objects detected from the image of the unit video data is 2, a token movement of 2 may be generated. In this way, if the token movement amount is defined according to the object detection result, the inflection point at which the number of objects in the image changes can be easily detected through the amount of change per transaction in the amount of tokens received at the receiving address.

As another example, the transaction generating unit 362 defines a transaction for moving a token from a sending address to a receiving address corresponding to each unit video data of video information, but determines whether or not an object is detected from the image of the unit video data, and the detected object. It may be configured to change the moving direction of the token according to the type of and/or the number of detected objects. For example, when an object is detected from an image of unit video data, a token is moved from a send address to a receive address, but when an object is not detected from an image, a transaction is executed to move a token by exchanging the send and receive addresses. can be defined Alternatively, when the first type of object (eg, male) is detected from the image of the unit video data, the token is moved from the sending address to the receiving address, and when a second type of object (eg, female) different from the sending address is detected, Transactions can be defined to move tokens by swapping the send and receive addresses. In this way, if the token movement amount is defined according to the object detection result, an inflection point where the type or presence of an object in the image changes can be easily detected through an increase or decrease in the amount of tokens received at the destination address.

As another example, along with receiving image information through the input unit 31, the user management unit 37 receives user information of a user who has taken a corresponding image or manages the corresponding image, and the transaction generator 362 receives each unit image. A transaction to be recorded in the same data block as the hash value of the corresponding unit image data may be defined by reflecting user information corresponding to the data. For example, the user management unit 37 stores each user's electronic wallet address for a plurality of preset users, and the transaction generation unit 362 records the hash value of each unit image data in a data block. A transaction defining the movement of tokens can be recorded together with the user's electronic wallet address corresponding to the unit video data as the sending address or receiving address. In this case, personal information such as a video manager or a photographer can be easily identified simply by moving the token through the sender who transmits the token through a transaction or the wallet address of the sender who transmits the token.

5 is a conceptual diagram for explaining video monitoring through a blockchain-based token transaction in a method for preventing forgery and falsification of a video based on a blockchain according to an embodiment. is shown according to At this time, the quantity of tokens was changed by the transaction recorded in the data block along with the hash value of the video data.

As shown in FIG. 5, in the first section 501, the number of tokens constantly increased over time, but in the next section 502, the gradient of increasing tokens changed, and then again in the next section 503. This increasing slope decreased. This means that if the first quantity of tokens per hour is deposited in intervals 501 and 503, the second quantity of tokens per hour greater than the first quantity is deposited in interval 502. When the amount of movement of tokens through transactions is defined according to the type or number of objects in the image of video data, the amount of movement of tokens per hour between intervals 501 and 502 and between intervals 502 and 503 Through this change, it can be known that a change has occurred in the object detected at the corresponding inflection point.

In addition, in the intervals 501 to 503, the number of tokens of the receiving address increased over time, which means that tokens were continuously deposited from other sending addresses to the corresponding receiving address. On the other hand, in the subsequent section 504, the number of tokens of the receiving address decreases over time, which means that the receiving address is no longer a receiver but is converted to a sender and tokens are withdrawn from the corresponding address. Therefore, when the direction of movement of the token through the transaction is defined according to the type or number of objects in the image of the video data, the object detected at the inflection point through the change in the form of the transaction between section 503 and section 504 It can be seen that a change has occurred in

Referring back to FIGS. 1 and 2 , in one embodiment, the storage unit 36 may further store one or more unit image data constituting image information or image information itself (S9). Although the process of storing image information (S9) in FIG. 2 is illustrated as being performed after the process of storing a hash value for preventing forgery and alteration of image information, this is an example for convenience of description and hash value for preventing forgery and alteration of image information. Image information may be stored in the storage prior to the storage of the hash value or in parallel with the storage of the hash value.

According to the block chain-based image forgery prevention method according to the embodiments described above, image data such as a video file is divided into predetermined unit image data, and the hash value of each unit image data is stored in a data block on a blockchain network for each time period. By storing in the form of a chain, when there is forgery or manipulation of image data, it is possible to recognize that the image has been manipulated through the hash value of the image data being different from the hash value previously stored on the blockchain. Through this, forgery and alteration of image data can be prevented, and reliability can be increased when images are used as evidence for illegal acts such as crimes and incidents.

The operation by the block chain-based video forgery prevention method according to the above-described embodiments may be at least partially implemented as a computer program and recorded on a computer-readable recording medium. The program for implementing the operation by the block chain-based image forgery prevention method according to the embodiments is recorded and the computer-readable recording medium includes all kinds of recording devices in which computer-readable data is stored. . Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. In addition, computer-readable recording media may be distributed in computer systems connected through a network, and computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing this embodiment can be easily understood by those skilled in the art to which this embodiment belongs.

The present invention reviewed above has been described with reference to the embodiments shown in the drawings, but this is only exemplary, and those skilled in the art will understand that various modifications and variations of the embodiments are possible therefrom. However, such modifications should be considered within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (13)

an input unit configured to receive image information from a photographing device;
an image division unit configured to divide the image information into a plurality of unit image data;
a hash extractor configured to calculate a hash value of each of the plurality of unit image data; and
A hash value of each of the plurality of unit image data is recorded in data blocks on a blockchain network, each of the data blocks including a hash value of a preceding data block and a hash value of the unit image data, and A storage unit configured to record a hash value of unit image data that precedes the unit image data corresponding to each data block among unit image data in a data block that precedes each data block,
Further comprising a machine learning unit configured to detect objects corresponding to the images by using the images included in the image information as input values for a machine learning-based analysis model,
The image segmentation unit specifies an image in which one or more of the type of object detected by the machine learning unit and the number of detected objects is changed, and divides the front and back of the specified image in the image information into different unit image data. more configured to
The storage unit is further configured to record transaction information for a token on the blockchain network defined corresponding to the unit image data in data blocks on the blockchain network together with the unit image data,
The storage unit, based on the type or number of objects detected by the machine learning unit for the image included in the unit image data, input/output history of the token defined by the transaction information or the token defined by the transaction information is further configured to determine one or more of the quantities;
A blockchain-based video forgery prevention system capable of monitoring, managing, and monitoring video information through transaction information by defining transaction information by reflecting the characteristics of the unit video data.
delete delete delete According to claim 1,
Further comprising a user management unit configured to receive user information corresponding to at least a part of the image information, including wallet address information for each user registered in advance;
The storage unit determines a wallet address for the unit image data corresponding to each data block based on the user information, and defines the transaction information using the determined wallet address as a sender or a recipient Blockchain-based 's video forgery prevention system.
According to claim 1,
The storage unit is a blockchain-based image forgery prevention system further configured to store the image information in an external storage.
A block chain-based image forgery prevention system receiving image information from a photographing device;
Dividing the image information into a plurality of unit image data by the blockchain-based image forgery prevention system;
calculating, by the blockchain-based image forgery prevention system, a hash value of each of the plurality of unit image data; and
The blockchain-based video forgery prevention system includes recording a hash value of each of the plurality of unit image data in data blocks on a blockchain network,
Each of the data blocks includes a hash value of a preceding data block and a hash value of the unit image data,
In the step of recording data blocks on the blockchain network, the block chain-based image forgery prevention system performs unit image data that precedes the unit image data corresponding to each data block among the plurality of unit image data. Recording the hash value of in a data block preceding each data block,
Before the step of dividing into a plurality of unit image data, the block chain-based image forgery prevention system uses the images included in the image information as input values for a machine learning-based analysis model to obtain corresponding images for the images. Further comprising the step of detecting the object,
In the step of dividing into a plurality of unit image data,
specifying, by the blockchain-based image forgery prevention system, an image in which at least one of the number of detected objects and the type of objects detected by the machine learning-based analysis model is changed; and
The block chain-based image forgery prevention system includes dividing the front and back of the specified image in the image information into different unit image data,
The step of the blockchain-based image forgery prevention system recording transaction information about a token on the blockchain network defined corresponding to the unit image data in data blocks on the blockchain network together with the unit image data. contain more,
In the step of recording the transaction information in the data blocks on the blockchain network, the blockchain-based image forgery prevention system detects the image included in the unit image data by the machine learning-based analysis model. Based on the type or number of objects, determining at least one of a token input/output history defined by the transaction information or a quantity of tokens defined by the transaction information,
A block chain-based video forgery prevention method that can monitor, manage, and monitor video information through transaction information by defining transaction information by reflecting the characteristics of unit video data.
delete delete delete According to claim 7,
Storing wallet address information for each user pre-registered in the blockchain-based video forgery prevention system; and
The blockchain-based image forgery prevention system further comprises receiving user information corresponding to at least a part of the image information,
The step of recording the transaction information in data blocks on the blockchain network,
determining, by the blockchain-based image forgery prevention system, a wallet address for the unit image data corresponding to each data block based on the user information; and
The block chain-based video forgery prevention system comprising the step of defining the transaction information using the determined wallet address as a sender or a receiver.
According to claim 7,
The block chain-based image forgery prevention method further comprising the step of storing the image information in an external storage by the blockchain-based image forgery prevention system.
A computer program stored in a computer-readable recording medium to be combined with hardware to execute the block chain-based image forgery prevention method according to any one of claims 7, 11, and 12.

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