WO2021235798A1 - Dispositif électronique et procédé de réalisation d'une authentification utilisateur à l'aide d'une entrée sur le clavier dans le dispositif électronique - Google Patents

Dispositif électronique et procédé de réalisation d'une authentification utilisateur à l'aide d'une entrée sur le clavier dans le dispositif électronique Download PDF

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WO2021235798A1
WO2021235798A1 PCT/KR2021/006152 KR2021006152W WO2021235798A1 WO 2021235798 A1 WO2021235798 A1 WO 2021235798A1 KR 2021006152 W KR2021006152 W KR 2021006152W WO 2021235798 A1 WO2021235798 A1 WO 2021235798A1
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Prior art keywords
input
value
electronic device
keyboard
classifier
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PCT/KR2021/006152
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English (en)
Korean (ko)
Inventor
허준호
포포프올렉산드르
곽성수
김일주
Original Assignee
삼성전자 주식회사
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Priority claimed from KR1020210045652A external-priority patent/KR20210142535A/ko
Application filed by 삼성전자 주식회사 filed Critical 삼성전자 주식회사
Publication of WO2021235798A1 publication Critical patent/WO2021235798A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/316—User authentication by observing the pattern of computer usage, e.g. typical user behaviour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
    • G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means

Definitions

  • Various embodiments relate to an electronic device and a method of performing user authentication using a keyboard input in the electronic device.
  • the user authentication method includes a method of requiring the user to authenticate once during initial login and a continuous authentication method of continuously verifying the authenticity of the user while the session is active.
  • the continuous authentication method includes a continuous authentication method through a keyboard input that continuously checks the authenticity of the user through the user's keyboard input while the user types the keyboard displayed on the touch screen.
  • the accuracy is determined by the equal error rate (EER), and the equal error rate (EER) is the false rejection rate (FRR) and the false recognition rate ( False Acceptance Rate: Indicates the error rate when FAR) is the same.
  • False Rejection Rate measures the error rate when a user's keyboard input behavior is incorrectly classified as an attacker's behavior
  • FAR False Acceptance Rate
  • an electronic device and a method of performing user authentication using a keyboard input in the electronic device are provided.
  • An electronic device includes a sensor module including a plurality of sensors, a display including a touch screen, and a processor, wherein the processor is configured to include, while using a keyboard displayed on the touch screen, the plurality of electronic devices.
  • a method of performing user authentication using a keyboard input in an electronic device includes a plurality of sensors detected according to an input of the keyboard using a plurality of sensors while using a keyboard displayed on a touch screen. checking values, detecting a first feature by calculating a correlation between the plurality of sensor values, inputting the plurality of sensor values as input values into a first learning model, and inputting the input from the first learning model When an output value is generated based on a value, detecting a second feature by calculating a difference between the input value and the output value, the first result value of the first classifier learning the first feature, and the second feature It may include an operation of determining a final authentication value based on a first result value of the second classifier that has learned , and an operation of authenticating a user using the keyboard based on the final authentication value.
  • FIG. 1 is a block diagram of an electronic device in a network environment according to various embodiments of the present disclosure
  • FIG. 2 is a block diagram illustrating an electronic device according to various embodiments of the present disclosure
  • FIG. 3 is a diagram for describing a user's authentication processing operation according to various embodiments of the present disclosure
  • FIG. 4 is a flowchart illustrating a learning operation for user authentication in an electronic device according to various embodiments of the present disclosure
  • FIG. 5 is a flowchart illustrating a user authentication operation in an electronic device according to various embodiments of the present disclosure
  • FIG. 1 is a block diagram 100 of an electronic device 101 in a network environment 100 according to various embodiments.
  • the electronic device 101 communicates with the electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or a second network 199 . It may communicate with at least one of the electronic device 104 and the server 108 through (eg, a long-distance wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 .
  • the electronic device 101 includes a processor 120 , a memory 130 , an input module 150 , a sound output module 155 , a display module 160 , an audio module 170 , and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or an antenna module 197 .
  • at least one of these components eg, the connection terminal 178
  • some of these components are integrated into one component (eg, display module 160 ). can be
  • the processor 120 executes software (eg, the program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120 . It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in the volatile memory 132 , and may process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 .
  • software eg, the program 140
  • the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 .
  • the volatile memory 132 may be stored in the volatile memory 132 , and may process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 .
  • the processor 120 is the main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit) a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor).
  • the main processor 121 e.g, a central processing unit or an application processor
  • a secondary processor 123 eg, a graphic processing unit, a neural network processing unit
  • NPU neural processing unit
  • an image signal processor e.g., a sensor hub processor, or a communication processor.
  • the main processor 121 e.g, a central processing unit or an application processor
  • a secondary processor 123 eg, a graphic processing unit, a neural network processing unit
  • NPU neural processing unit
  • an image signal processor e.g., a sensor hub processor, or a communication processor.
  • the main processor 121 e.g, a central processing unit or an application processor
  • a secondary processor 123
  • the auxiliary processor 123 may be, for example, on behalf of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or when the main processor 121 is active (eg, executing an application). ), together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states.
  • the coprocessor 123 eg, an image signal processor or a communication processor
  • may be implemented as part of another functionally related component eg, the camera module 180 or the communication module 190. have.
  • the auxiliary processor 123 may include a hardware structure specialized for processing an artificial intelligence model.
  • Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108).
  • the learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited
  • the artificial intelligence model may include a plurality of artificial neural network layers.
  • Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example.
  • the AI model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.
  • the memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176 ) of the electronic device 101 .
  • the data may include, for example, input data or output data for software (eg, the program 140 ) and instructions related thereto.
  • the memory 130 may include a volatile memory 132 or a non-volatile memory 134 .
  • the program 140 may be stored as software in the memory 130 , and may include, for example, an operating system 142 , middleware 144 , or an application 146 .
  • the input module 150 may receive a command or data to be used by a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 .
  • the input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).
  • the sound output module 155 may output a sound signal to the outside of the electronic device 101 .
  • the sound output module 155 may include, for example, a speaker or a receiver.
  • the speaker can be used for general purposes such as multimedia playback or recording playback.
  • the receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from or as part of the speaker.
  • the display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 .
  • the display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device.
  • the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.
  • the audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 or an external electronic device (eg, a sound output module 155 ) directly or wirelessly connected to the electronic device 101 .
  • the electronic device 102) eg, a speaker or headphones
  • the sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, user state), and generates an electrical signal or data value corresponding to the sensed state. can do.
  • the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.
  • the interface 177 may support one or more specified protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, the electronic device 102 ).
  • the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.
  • the connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102 ).
  • the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).
  • the haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense.
  • the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.
  • the camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.
  • the power management module 188 may manage power supplied to the electronic device 101 .
  • the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).
  • PMIC power management integrated circuit
  • the battery 189 may supply power to at least one component of the electronic device 101 .
  • battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.
  • the communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication through the established communication channel.
  • the communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication.
  • the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : It may include a local area network (LAN) communication module, or a power line communication module).
  • a corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy).
  • a first network 198 eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)
  • a second network 199 eg, legacy
  • the wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 .
  • the electronic device 101 may be identified or authenticated.
  • the wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, a new radio access technology (NR).
  • NR access technology includes high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency) -latency communications)).
  • eMBB enhanced mobile broadband
  • mMTC massive machine type communications
  • URLLC ultra-reliable and low-latency
  • the wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate.
  • a high frequency band eg, mmWave band
  • the wireless communication module 192 includes various technologies for securing performance in a high-frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), all-dimensional multiplexing. It may support technologies such as full dimensional MIMO (FD-MIMO), an array antenna, analog beam-forming, or a large scale antenna.
  • the wireless communication module 192 may support various requirements specified in the electronic device 101 , an external electronic device (eg, the electronic device 104 ), or a network system (eg, the second network 199 ).
  • the wireless communication module 192 may include a peak data rate (eg, 20 Gbps or more) for realizing eMBB, loss coverage (eg, 164 dB or less) for realizing mMTC, or U-plane latency for realizing URLLC ( Example: downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) may be supported.
  • a peak data rate eg, 20 Gbps or more
  • loss coverage eg, 164 dB or less
  • U-plane latency for realizing URLLC
  • the antenna module 197 may transmit or receive a signal or power to the outside (eg, an external electronic device).
  • the antenna module 197 may include an antenna including a conductor formed on a substrate (eg, a PCB) or a radiator formed of a conductive pattern.
  • the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected from the plurality of antennas by, for example, the communication module 190 . can be selected. A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna.
  • other components eg, a radio frequency integrated circuit (RFIC)
  • RFIC radio frequency integrated circuit
  • the antenna module 197 may form a mmWave antenna module.
  • the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side surface) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.
  • peripheral devices eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)
  • GPIO general purpose input and output
  • SPI serial peripheral interface
  • MIPI mobile industry processor interface
  • the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 .
  • Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 .
  • all or a part of operations executed in the electronic device 101 may be executed in one or more external electronic devices 102 , 104 , or 108 .
  • the electronic device 101 may perform the function or service itself instead of executing the function or service itself.
  • one or more external electronic devices may be requested to perform at least a part of the function or the service.
  • One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 .
  • the electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request.
  • cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used.
  • the electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing.
  • the external electronic device 104 may include an Internet of things (IoT) device.
  • the server 108 may be an intelligent server using machine learning and/or neural networks.
  • the external electronic device 104 or the server 108 may be included in the second network 199 .
  • the electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.
  • FIG. 2 is a block diagram 200 illustrating an electronic device according to various embodiments of the present disclosure.
  • the electronic device 201 may include a processor 220 , a memory 230 , a display 260 , and a sensor module 276 . have.
  • the processor 220 may control the overall operation of the electronic device 201 , and may be the same as the processor 120 of FIG. 1 , or at least one It can perform a function or action.
  • the processor 220 for setting the continuous authentication mode through the keyboard input, the first learning model learned based on general user data, and optimization hyperparameters capable of optimizing the classifier for each type (optimal hyperparameters) can be checked.
  • the first learning model may include an autoencoder, which is a deep learning learning model that detects features while compressing the original data through an encoder and then recovering it through a decoder again.
  • the autoencoder is a model in which the number of output values and input values is the same in the form of a neural network. It is composed of an input/output layer that is symmetrical left and right, and compresses the original data through the encoder and then recovers it through the decoder. It is a learning model that extracts features with
  • the optimization hyperparameter is for optimizing for each type of a classifier (eg, a binary classification model) that classifies features, and is learned to distinguish a user of the electronic device from a user other than the user of the electronic device.
  • the hyperparameter optimized for each type of classifier may be stored in advance in the memory 230 of the electronic device.
  • the processor 220 may collect initial user data of the electronic device for activating the continuous authentication mode through a keyboard input.
  • the processor 220 when the user of the electronic device tries to activate the continuous authentication mode through a keyboard input for the first time in the electronic device, the processor 220 provides a plurality of arbitrary sentences in various operations to the user of the electronic device. You can ask for input.
  • the processor 220 sets a plurality of sensor values detected through a plurality of sensors of the sensor module 276 while the user of the electronic device performs a key input on the keyboard for input of a plurality of arbitrary sentences as initial user data. can be collected with
  • the processor 220 may request that the user of the electronic device write a plurality of arbitrary sentences (about 5 sentences) given in various operations including sitting, walking, and/or reclining, etc.
  • a plurality of sensor values detected through a plurality of sensors of the sensor module 276 are collected as initial user data while the user performs key input about 250 to 300 times through the keyboard. can do.
  • the processor 220 may additionally collect initial user data based on a key input operation of the user using the keyboard of the electronic device for 1 to 2 days after collecting the initial user data. have.
  • the processor 220 includes a first feature capable of learning the first classifiers based on initial user data indicating a plurality of sensor values detected through a plurality of sensors of the sensor module 276 . (eg Correlation features) can be detected.
  • the processor 220 may detect a characteristic of a correlation between the plurality of sensor values representing the initial user data as a first characteristic capable of learning a first classifier.
  • the processor 220 uses Pearson's rank correlation and/or Spearman's rank correlation to detect a plurality of sensors from each other. It is possible to calculate the characteristic of the linear correlation between the sensor values of .
  • the processor 220 may include first sensor values X1, Y1, Z1, and M1 detected by a first sensor (eg, an acceleration sensor) and a second sensor (eg, a gyro sensor) detected by the second sensor. From the two sensor values (X2, Y2, Z2, M2), the characteristics of the correlation between (X1, X2), (Y1, Y2), (Z1, Z2) and (M1, M2) can be calculated.
  • a first sensor eg, an acceleration sensor
  • a second sensor eg, a gyro sensor
  • the processor 220 may include four values (X1, Y1, Z1, M1) included in each sensor value detected from each of three sensors (eg, an acceleration sensor, a gyro sensor, and a touch sensor). In the case of calculating the correlation for , it is possible to calculate a total of 48 correlation features.
  • the first classifier may include a binary classification model.
  • the processor 220 may output an input value and an output of the first learning model based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in the sensor module 276 .
  • a difference between values may be calculated, and the calculated difference may be detected as a second feature (eg, Decoder-DTW features) capable of training a second classifier.
  • the processor 220 when a key of the keyboard is input by the user, the processor 220 is configured to "down" the key for several seconds (eg, 100 milliseconds) before the time point. Any sensor values detected from the plurality of sensors (eg 36 sensor values) and any sensor detected from the plurality of sensors for a few seconds (eg 100 milliseconds) after the key is “down” Values (eg, 36 sensor values) can be obtained.
  • the processor 220 may determine that a plurality of sensor values (eg, 14 sensor values) acquired from the plurality of sensors according to a key input in the initial data collection process and the key are “down ( down)", a plurality of sensor values (eg, 72 sensor values) acquired before and after the time point may be determined as an input value of the first learning model.
  • a plurality of sensor values eg, 14 sensor values
  • a plurality of sensor values eg, 72 sensor values
  • the first learning model is a model in which the number of output values and the number of input values is the same. (eg, auto encoder) can generate and output the same output value (14 x 72 sensor value) as the input value and the number.
  • the processor 220 uses fast dynamic time warping (DTW) for calculating the similarity between the input value and the output value of the first learning model (eg, autoencoder).
  • DTW fast dynamic time warping
  • a difference may be detected, and the difference may be detected as the second characteristic.
  • the second classifier may include a binary classification model.
  • the processor 220 based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in the sensor module 276 , a touch pressure according to a key input of the keyboard
  • the value can be detected as a third feature (eg, HMOG grasp resistance (HMOG-GR) features) that can learn the third classifier.
  • HMOG-GR HMOG grasp resistance
  • the processor 220 may detect, as the third feature, an average and standard deviation of a plurality of sensor values acquired from a plurality of sensors while a key is input.
  • the third classifier may include a binary classification model.
  • the processor 220 may perform a touch input according to a key input of the keyboard based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in the sensor module 276 .
  • Time, touch size, and touch coordinates may be detected as uni-graph features capable of learning the fourth classifier.
  • the fourth classifier may include a binary classification model.
  • the processor 220 may learn by inputting each of a plurality of features into a plurality of corresponding classifiers.
  • the processor 220 is configured to learn by inputting the first feature to the first classifier, input the second feature to the second classifier to learn, and apply the third feature to the second classifier.
  • the third classifier may be input to learn, and the fourth characteristic may be inputted to the fourth classifier to learn.
  • the processor 220 uses, as a classification feature, a second feature representing a difference between the input value and the output value of the first learning model (eg, autoencoder) as a transfer learning feature. ) through which the second classifier can be trained.
  • a second feature representing a difference between the input value and the output value of the first learning model (eg, autoencoder) as a transfer learning feature. ) through which the second classifier can be trained.
  • the processor 220 while using the keyboard displayed on the touch screen included in the display 260, in a state in which the continuous authentication mode through the keyboard input is activated, the first classifier, the second The user using the keyboard may be authenticated based on a result value output from at least one of the classifier, the third classifier, and the fourth classifier.
  • the processor 220 while using the keyboard displayed on the touch screen included in the display 260, according to the input of the keyboard from a plurality of sensors included in the sensor module 276 Detect a plurality of acquired sensor values as user data, and detect as at least one of a first characteristic, a second characteristic, a third characteristic, and a fourth characteristic based on the plurality of sensor values, and select the at least one characteristic
  • the input may be performed as at least one of a first classifier, a second classifier, a third classifier, and a fourth classifier.
  • the processor 220 uses Pearson's rank correlation and/or Spearman's rank correlation, and the plurality of sensors representing the user data. A characteristic of a correlation between values may be detected as the first characteristic.
  • the processor 220 detects an input value and an output value of a first learning model (eg, an autoencoder) using the plurality of sensor values representing the user data, and performs Fast DTW (fast DTW). Dynamic time warping) may be used to calculate a difference between the input value and the output value, and the calculated difference may be detected as a second feature (eg, decoder-DTW features).
  • a first learning model eg, an autoencoder
  • Dynamic time warping may be used to calculate a difference between the input value and the output value, and the calculated difference may be detected as a second feature (eg, decoder-DTW features).
  • the processor 220 may include a third characteristic (eg, HMOG grasp resistance (HMOG- GR) features).
  • HMOG- GR HMOG grasp resistance
  • the processor 220 may include a fourth feature indicating a touch input time according to a key input of the keyboard, a touch size of a touch, and a touch coordinate based on the plurality of sensor values indicating the user data. (Uni-graph features) can be detected.
  • the processor 220 detects the first characteristic and the second characteristic based on a plurality of sensor values obtained from a plurality of sensors included in the sensor module 276 according to the input of the keyboard. and input the first feature to the first classifier, input the second feature to the second classifier, and a first result value output from the first classifier and a second result value output to the second classifier
  • An average score for ? may be determined as a final authentication value for authentication of a user using the keyboard.
  • the processor 220 is configured to perform a first feature, a second feature, and a second feature based on a plurality of sensor values obtained from a plurality of sensors included in the sensor module 276 according to the input of the keyboard. Detect a third feature and a fourth feature, input the first feature to the first classifier, input the second feature to the second classifier, input the third feature to the third classifier, and 4 features may be input into the fourth classifier.
  • the processor 220 is configured to output a first result value output from the first classifier, a second result value output to the second classifier, a third result value output to the third classifier, and output to the fourth classifier. An average score for the fourth result value may be determined as a final authentication value for authentication of a user using the keyboard.
  • the processor 220 may compare the final authentication value with a threshold value, and if the final authentication value is equal to or greater than the threshold value, the user using the keyboard may authenticate as a user of the electronic device.
  • the processor 220 compares the final authentication value with a threshold value, and determines that the user using the keyboard is a user other than the user of the electronic device when the final authentication value is less than or equal to the threshold value.
  • the processor 220 when the user who uses the keyboard is identified as a user other than the user of the electronic device, the processor 220 performs an authentication method (eg, a password) used to unlock the electronic device; Another authentication method (eg, fingerprint recognition) can be used to request re-authentication from the user.
  • an authentication method eg, a password
  • Another authentication method eg, fingerprint recognition
  • the processor 220 determines that the current application being executed is lower than the threshold value. transmitted, so that the application can determine whether to forcibly terminate the application.
  • the memory 230 may be implemented substantially the same as or similar to the memory 130 of FIG. 1 .
  • the memory 230 includes a plurality of classification models (eg, binary classification models), a plurality of learning models (eg, autoencoder), user data, and optimization hyperparameters for continuous authentication through keyboard input. can be saved.
  • classification models eg, binary classification models
  • learning models eg, autoencoder
  • user data e.g., user data
  • optimization hyperparameters for continuous authentication through keyboard input can be saved.
  • the display 260 may be implemented substantially the same as or similar to the display module 160 of FIG. 1 .
  • the sensor module 276 may be implemented substantially the same as or similar to the sensor module module 176 of FIG. 1 .
  • the sensor module 276 may include a plurality of sensors.
  • the sensor module 276 may include an acceleration sensor, a gyro sensor, and a touch sensor.
  • the sensor module 276 may detect a plurality of sensor values according to a key input through the keyboard while using the keyboard displayed on the touch screen included in the display 260 .
  • FIG. 3 is a diagram 300 for explaining a user's authentication processing operation according to various embodiments of the present disclosure.
  • the processor (eg, the processor 120 of FIG. 1 and/or the processor 220 of FIG. 2 ) operates in a state in which a continuous authentication mode through a keyboard input is activated. Acquired based on a plurality of sensors included in a sensor module (eg, the sensor module 176 of FIG. 1 and/or the sensor module 276 of FIG. 2 ) according to a key input while using the keyboard displayed on the touch screen A plurality of sensor values may be detected as user data 310 .
  • a sensor module eg, the sensor module 176 of FIG. 1 and/or the sensor module 276 of FIG. 2
  • the processor may include Pearson's rank correlation or/and Spearman's rank correlation (A linear correlation between a plurality of sensor values representing the user data 310 may be calculated using Spearman's rank correlation, and the calculated correlation may be detected as the first feature 331 .
  • the processor may input the first feature 331 into the first classifier 351 and check a first result value 371 of the first classifier 351 to which the first feature 331 is input. have.
  • the processor receives a key from the sensor module according to a key input while using the keyboard displayed on the touch screen.
  • User data representing a plurality of detected sensor values and a plurality of sensor values (eg, 72 sensor values) acquired before and after the key is “down” on the keyboard displayed on the touch screen are detected as input values and an output value generated when the first input value is input to a first learning model (eg, an auto encoder) may be detected.
  • the processor may detect a difference between the input value and the output value using fast dynamic time warping (DTW) for calculating the similarity, and detect the difference as the second feature 333 .
  • the processor may input the second feature 333 into a second classifier 353 and check a second result value 373 of the second classifier 353 to which the second feature 333 is input. have.
  • DTW fast dynamic time warping
  • the processor receives a key from the sensor module according to a key input while using the keyboard displayed on the touch screen.
  • User data representing a plurality of sensor values may be detected, and a touch pressure value according to a key input of the keyboard detected based on the user data may be detected as the third feature 335 .
  • the processor may input the third feature 335 into the third classifier 355 and check a third result value 375 of the third classifier 355 to which the third feature 335 is input. have.
  • the processor receives a key from the sensor module according to a key input while using the keyboard displayed on the touch screen. It is possible to detect user data representing a plurality of sensor values, and detect a touch input time according to a key input of the keyboard detected based on the user data, a touch size of a touch, and a touch coordinate as the fourth feature 337 . have.
  • the processor may input the fourth feature 337 into a fourth classifier 357 and check a fourth result value 377 of the fourth classifier 353 to which the fourth feature 337 is input. have.
  • the processor may perform an average of the first result value 371 and the second result value 373 .
  • the score is detected as the final authentication value, or the average acceptance of the first result value 371 , the second result value 373 , the third result value 375 , and the fourth result value 377 is obtained. It can be detected as the final authentication value.
  • the processor may allow a user using a keyboard displayed on the touch screen by comparing the final authentication value and the threshold value. Whether the user is a user of the electronic device or a user other than the user of the electronic device may be checked.
  • the electronic device (eg, the electronic device 201 of FIG. 2 ) includes a sensor module (eg, the sensor module 276 ) including a plurality of sensors, and a display (eg, FIG. 2 ) including a touch screen. display 260 of 2); and a processor (eg, the processor 220 of FIG. 2 ), wherein the processor includes a plurality of sensors detected according to an input of the keyboard using the plurality of sensors while using a keyboard displayed on the touch screen.
  • Checking sensor values calculating a correlation between the plurality of sensor values to detect a first feature, inputting the plurality of sensor values as input values to a first learning model, and the input value from the first learning model
  • an output value is generated based on It may be configured to determine a final authentication value based on a first result value of a second classifier, and to authenticate a user using the keyboard based on the final authentication value.
  • the processor may be configured to detect the first characteristic by calculating a correlation between the plurality of sensor values using a Pearson correlation or a Spearman correlation.
  • the processor when a key of the keyboard is input, the processor may be configured to determine a predetermined number of sensor values before and after the key input and the plurality of sensor values as the input values of the first learning model.
  • the first learning model may include an autoencoder.
  • the processor may detect the second characteristic by detecting a difference between the input value and the output value using fast dynamic time warping (DTW).
  • DTW fast dynamic time warping
  • the first classifier and the second classifier may include a binary classifier.
  • a touch pressure value according to a key input of the keyboard is detected as a third feature based on the plurality of sensor values, and a touch according to a key input of the keyboard is detected based on the plurality of sensor values.
  • the third result value and the fourth feature of a third classifier that detects the input time, the touch size, and the touch coordinate as the fourth feature, and learns the first result value, the second result value, and the third feature It may be set to determine the final authentication value based on the fourth result value of the fourth classifier that has learned .
  • the third classifier and the fourth classifier may include a binary classifier.
  • the plurality of sensors may include an acceleration sensor, a gyro sensor, and a touch sensor.
  • the processor may be configured to authenticate the user using the keyboard by comparing the final authentication value with a threshold value.
  • the learning operation for user authentication may include operations 401 to 405 and may include a processor (eg, the processor of FIG. 1 ) of an electronic device (eg, the electronic device 101 of FIG. 1 or the electronic device 201 of FIG. 2 ). 120 or the processor 220 of FIG. 2). According to an embodiment, at least one of operations 401 to 405 may be omitted, the order of some operations may be changed, or another operation may be added.
  • the electronic device may collect initial user data of the electronic device for activating the continuous authentication mode through a keyboard input.
  • the electronic device when the user of the electronic device tries to activate the continuous authentication mode through a keyboard input for the first time in the electronic device, the electronic device inputs a plurality of arbitrary sentences in various operations to the user of the electronic device can request
  • the electronic device includes a plurality of sensor values detected through a plurality of sensors of a sensor module (eg, the sensor module 276 ) while a user of the electronic device performs a key input of a keyboard for input of a plurality of arbitrary sentences. can be collected as initial user data.
  • the electronic device may additionally collect initial user data based on a key input operation of the user using the keyboard of the electronic device for 1 to 2 days after collecting the initial user data.
  • the electronic device may detect a plurality of features based on initial data.
  • the electronic device uses a sensor module (eg, sensor module 276) using Pearson's rank correlation and/or Spearman's rank correlation. It is possible to calculate a characteristic of a linear correlation between a plurality of sensor values (eg, initial user data) detected by each of the plurality of sensors of , and detect the characteristic of the calculated correlation as a first characteristic.
  • a sensor module eg, sensor module 276
  • Pearson's rank correlation and/or Spearman's rank correlation It is possible to calculate a characteristic of a linear correlation between a plurality of sensor values (eg, initial user data) detected by each of the plurality of sensors of , and detect the characteristic of the calculated correlation as a first characteristic.
  • the electronic device may include a first learning model (eg, an autoencoder) based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in the sensor module 276 . input and output values can be detected.
  • the electronic device detects a difference between the input value and the output value of the first learning model (eg, auto encoder) using fast dynamic time warping (DTW), and uses the difference as the second feature. can be detected.
  • DTW fast dynamic time warping
  • the electronic device inputs a key of the keyboard based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in a sensor module (eg, the sensor module 276). It is possible to detect the touch pressure value according to the third feature (eg, HMOG grasp resistance (HMOG-GR) features) that can learn the third classifier.
  • a sensor module eg, the sensor module 276
  • the electronic device may include, based on initial user data indicating a plurality of sensor values detected from a plurality of sensors included in the sensor module, a touch input time according to a key input of the keyboard, a touch
  • the touch size and touch coordinates may be detected as uni-graph features capable of learning the fourth classifier.
  • the electronic device may learn a plurality of classifiers using a plurality of features.
  • the electronic device is configured to learn by inputting the first feature into the first classifier, input the second feature into the second classifier to learn, and apply the third feature to the third classifier It is possible to learn by inputting and learning by inputting the fourth characteristic into the fourth classifier.
  • the user authentication operation may include operations 501 to 505 and may include a processor (eg, the processor 120 of FIG. 1 ) of an electronic device (eg, the electronic device 101 of FIG. 1 or the electronic device 201 of FIG. 2 ). Alternatively, it may be understood as being performed by the processor 220 of FIG. 2 . According to an embodiment, at least one of operations 501 to 505 may be omitted, the order of some operations may be changed, or another operation may be added.
  • the electronic device receives user data while inputting the keyboard while the continuous authentication mode through the keyboard input is activated. can be collected
  • the electronic device may include a sensor module (eg, the sensor module ( 176) or/and a plurality of sensor values acquired based on a plurality of sensors included in the sensor module 276 of FIG. 2 ) may be detected as user data.
  • a sensor module eg, the sensor module ( 176) or/and a plurality of sensor values acquired based on a plurality of sensors included in the sensor module 276 of FIG. 2 .
  • the electronic device may detect a plurality of features based on user data.
  • the plurality of features may include a first feature, a second feature, a third feature, and/or a fourth feature.
  • the electronic device uses a Pearson's rank correlation and/or a Spearman's rank correlation to provide a linear relationship between a plurality of sensor values representing the user data.
  • a negative correlation may be calculated, and the calculated correlation may be detected as the first feature.
  • the electronic device detects, as input values, the user data and a plurality of sensor values acquired before and after a key is “downed” in the keyboard displayed on the touch screen, as input values, and the first An output value generated when an input value is input to the first learning model (eg, autoencoder) may be detected.
  • the electronic device may detect a difference between the input value and the output value using fast dynamic time warping (DTW), and detect the difference as the second characteristic.
  • DTW fast dynamic time warping
  • the electronic device may detect a touch pressure value according to a key input of the keyboard detected based on the user data as a third feature.
  • the electronic device may detect a touch input time according to a key input of the keyboard detected based on the user data, a touch size of a touch, and a touch coordinate as the fourth characteristic.
  • the electronic device (eg, the electronic device 101 of FIG. 1 or the electronic device 201 of FIG. 2 ) performs user authentication based on result values output from a plurality of classifiers to which a plurality of features are input. can do
  • At least one of the first feature, the second feature, the third feature, and the fourth feature included in the plurality of features is selected from at least one of a first classifier, a second classifier, a third classifier, and a fourth classifier. It can be entered as a single classifier.
  • the electronic device inputs the first feature to the first classifier, inputs the second feature to the second classifier, and includes a first result value output from the first classifier and the second classifier.
  • An average score for the second result value output to the second classifier may be determined as a final authentication value for authentication of a user using the keyboard.
  • the electronic device inputs the first feature into the first classifier, inputs the second feature into the second classifier, inputs the third feature into the third classifier, and A fourth characteristic may be input to the fourth classifier.
  • the electronic device may include a first result value output from the first classifier, a second result value outputted to the second classifier, a third result value outputted to the third classifier, and a fourth value outputted to the fourth classifier.
  • An average score for the result value may be determined as a final authentication value for authentication of a user using the keyboard.
  • the electronic device may compare the final authentication value with the threshold value, and when the final authentication value is determined to be equal to or greater than the threshold value, the user using the keyboard may authenticate as a user of the electronic device.
  • the electronic device compares the final authentication value with a threshold value, and when the final authentication value is determined to be less than or equal to the threshold value, confirms that the user using the keyboard is a user other than the user of the electronic device can
  • the electronic device when the user who uses the keyboard is identified as a user other than the user of the electronic device, the electronic device performs authentication different from the authentication method (eg, password) used to unlock the electronic device. You can request re-authentication from the user by a method (eg, fingerprint recognition).
  • the authentication method eg, password
  • a method eg, fingerprint recognition
  • the electronic device when the user using the keyboard identifies as a user other than the user of the electronic device, the electronic device transmits the final authentication value, which is confirmed to be lower than the threshold value, to the current application being executed. , it is possible to enable the application to determine whether to forcibly terminate the application.
  • a plurality of sensors detected according to a keyboard input using a plurality of sensors while using a keyboard displayed on a touch screen Checking sensor values, detecting a first feature by calculating a correlation between the plurality of sensor values, inputting the plurality of sensor values as input values into a first learning model, When an output value is generated based on an input value, detecting a second feature by calculating a difference between the input value and the output value, a first result value of a first classifier learning the first feature, and the second
  • the method may include an operation of determining a final authentication value based on a first result value of the second classifier having learned the characteristic, and an operation of authenticating a user using the keyboard based on the final authentication value.
  • the detecting of the first feature may include detecting the first feature by calculating a correlation between the plurality of sensor values using a Pearson correlation or a Spearman correlation.
  • the detecting of the second characteristic may include determining a predetermined number of sensor values before and after the key input and the plurality of sensor values as the input values of the first learning model when a key of the keyboard is input. It may include an action to
  • the first learning model may include an autoencoder.
  • the detecting of the second feature may include detecting the second feature by detecting a difference between the input value and the output value using fast dynamic time warping (DTW).
  • DTW fast dynamic time warping
  • the first classifier and the second classifier may include a binary classifier.
  • detecting a touch pressure value according to a key input of the keyboard as a third feature based on the plurality of sensor values, according to a key input of the keyboard
  • An operation of detecting a touch input time, a touch size of a touch, and a touch coordinate as a fourth characteristic, and the first result value, the second result value, a third result value of a third classifier learning the third characteristic, and the The method may further include determining the final authentication value based on a fourth result value of the fourth classifier having learned the fourth characteristic.
  • the third classifier and the fourth classifier may include a binary classifier.
  • the plurality of sensors may include an acceleration sensor, a gyro sensor, and a touch sensor.
  • the method may further include authenticating the user using the keyboard by comparing the final authentication value with a threshold value.
  • the electronic device may have various types of devices.
  • the electronic device may include, for example, a portable communication device (eg, a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance device.
  • a portable communication device eg, a smart phone
  • a computer device e.g., a smart phone
  • a portable multimedia device e.g., a portable medical device
  • a camera e.g., a portable medical device
  • a camera e.g., a portable medical device
  • a camera e.g., a portable medical device
  • a wearable device e.g., a smart bracelet
  • a home appliance device e.g., a home appliance
  • first”, “second”, or “first” or “second” may simply be used to distinguish the component from other such components, and refer to the component in another aspect (e.g., importance or order) is not limited. It is said that one (eg, first) component is “coupled” or “connected” to another (eg, second) component, with or without the terms “functionally” or “communicatively”. When referenced, it means that one component can be connected to the other component directly (eg by wire), wirelessly, or through a third component.
  • module used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as, for example, logic, logic block, component, or circuit.
  • a module may be an integrally formed part or a minimum unit or a part of the part that performs one or more functions.
  • the module may be implemented in the form of an application-specific integrated circuit (ASIC).
  • ASIC application-specific integrated circuit
  • Various embodiments of the present document include one or more instructions stored in a storage medium (eg, internal memory 136 or external memory 138) readable by a machine (eg, electronic device 101).
  • a machine eg, electronic device 101
  • the processor eg, the processor 120
  • the device eg, the electronic device 101
  • the one or more instructions may include code generated by a compiler or code executable by an interpreter.
  • the device-readable storage medium may be provided in the form of a non-transitory storage medium.
  • 'non-transitory' only means that the storage medium is a tangible device and does not contain a signal (eg, electromagnetic wave), and this term refers to the case where data is semi-permanently stored in the storage medium and It does not distinguish between temporary storage cases.
  • a signal eg, electromagnetic wave
  • the method according to various embodiments disclosed in this document may be provided in a computer program product (computer program product).
  • Computer program products may be traded between sellers and buyers as commodities.
  • the computer program product is distributed in the form of a machine-readable storage medium (eg compact disc read only memory (CD-ROM)), or via an application store (eg Play Store TM ) or on two user devices ( It can be distributed (eg downloaded or uploaded) directly or online between smartphones (eg: smartphones).
  • a part of the computer program product may be temporarily stored or temporarily created in a machine-readable storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.
  • each component eg, a module or a program of the above-described components may include a singular or a plurality of entities, and some of the plurality of entities may be separately disposed in other components. have.
  • one or more components or operations among the above-described corresponding components may be omitted, or one or more other components or operations may be added.
  • a plurality of components eg, a module or a program
  • the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. .
  • operations performed by a module, program, or other component are executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations are executed in a different order, or omitted. or one or more other operations may be added.

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Abstract

Un dispositif électronique selon divers modes de réalisation de l'invention comprend : un module de capteur contenant une pluralité de capteurs, un affichage contenant un écran tactile, et un processeur. Le processeur peut être configuré pour : pendant qu'un clavier affiché sur l'écran tactile est utilisé, identifier une pluralité de valeurs de capteur détectées selon une entrée sur le clavier, en utilisant la pluralité de capteurs ; détecter une première caractéristique en calculant la corrélation entre la pluralité de valeurs de capteur ; entrer, en tant que valeurs d'entrée, la pluralité de valeurs de capteur dans un premier modèle d'apprentissage ; lorsque le premier modèle d'apprentissage génère une valeur de sortie sur la base des valeurs d'entrée, détecter une seconde caractéristique en calculant la différence entre les valeurs d'entrée et la valeur de sortie ; déterminer une valeur d'authentification finale sur la base d'une première valeur de résultat provenant d'un premier classificateur qui a appris la première caractéristique et une première valeur de résultat à partir d'un second classificateur qui a appris la seconde caractéristique ; et authentifier un utilisateur qui utilise le clavier, sur la base de la valeur d'authentification finale. L'invention peut concerner différents autres modes de réalisation.
PCT/KR2021/006152 2020-05-18 2021-05-17 Dispositif électronique et procédé de réalisation d'une authentification utilisateur à l'aide d'une entrée sur le clavier dans le dispositif électronique WO2021235798A1 (fr)

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US202063026438P 2020-05-18 2020-05-18
US63/026,438 2020-05-18
KR20200095150 2020-07-30
KR10-2020-0095150 2020-07-30
KR10-2021-0045652 2021-04-08
KR1020210045652A KR20210142535A (ko) 2020-05-18 2021-04-08 전자 장치 및 전자 장치에서 키보드의 입력을 이용하여 사용자 인증을 수행하는 방법

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2477136A1 (fr) * 2011-01-17 2012-07-18 Deutsche Telekom AG Procédé pour la vérification continue de l'identité de l'utilisateur via des dynamiques de frappes
KR20170082778A (ko) * 2016-01-07 2017-07-17 한국전자통신연구원 사용자 자세에 기초한 키스트로크 패턴을 이용한 사용자 분류 장치 및 방법
US20170337364A1 (en) * 2016-05-19 2017-11-23 UnifyID Identifying and authenticating users based on passive factors determined from sensor data
KR20190018202A (ko) * 2017-08-14 2019-02-22 인터리젠 주식회사 키스트로크 패턴을 이용한 사용자 인증방법 및 장치
KR20190018197A (ko) * 2017-08-14 2019-02-22 인터리젠 주식회사 사용자 인증방법 및 장치

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2477136A1 (fr) * 2011-01-17 2012-07-18 Deutsche Telekom AG Procédé pour la vérification continue de l'identité de l'utilisateur via des dynamiques de frappes
KR20170082778A (ko) * 2016-01-07 2017-07-17 한국전자통신연구원 사용자 자세에 기초한 키스트로크 패턴을 이용한 사용자 분류 장치 및 방법
US20170337364A1 (en) * 2016-05-19 2017-11-23 UnifyID Identifying and authenticating users based on passive factors determined from sensor data
KR20190018202A (ko) * 2017-08-14 2019-02-22 인터리젠 주식회사 키스트로크 패턴을 이용한 사용자 인증방법 및 장치
KR20190018197A (ko) * 2017-08-14 2019-02-22 인터리젠 주식회사 사용자 인증방법 및 장치

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