WO2021092872A1 - 一种基于智能手机传感器的设备指纹提取方法 - Google Patents
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- 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
- G06F21/44—Program or device authentication
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Definitions
- the invention belongs to the field of mobile terminals.
- the device fingerprint extraction technology based on smart phone sensors mainly relates to the collection, inference, calibration and other operations of the sensor information of the smart phone, and specifically relates to the three types of sensors for gyroscopes, accelerometers, and magnetometers.
- the deviation gain calculation verification and the verification of the fingerprint generation of the smart phone device mainly relates to the collection, inference, calibration and other operations of the sensor information of the smart phone, and specifically relates to the three types of sensors for gyroscopes, accelerometers, and magnetometers.
- the deviation gain calculation verification and the verification of the fingerprint generation of the smart phone device mainly relates to the collection, inference, calibration and other operations of the sensor information of the smart phone, and specifically relates to the three types of sensors for gyroscopes, accelerometers, and magnetometers.
- the deviation gain calculation verification and the verification of the fingerprint generation of the smart phone device mainly relates to the collection, inference, calibration and other operations of the
- Sensors are an important component of smart phones. Every smart phone contains many sensor components, from cameras, microphones, light sensors to GPS, gyroscopes, accelerometers, etc., which are all based on MEMA (Mississippi Emergency Management Agency) technology. For a good sensor, accuracy is critical, so equipment manufacturers generally use factory calibration to compensate for deviations in the manufacturing process. Analyzing the output data of the sensor can calculate the calibration data of each sensor, and this calibration data can be used as the fingerprint of the device.
- MEMA Microsissippi Emergency Management Agency
- smart phones mainly include two major operating systems: Android and iOS.
- the fingerprint extraction of traditional Android system devices mainly relies on IMEI, MAC, ANDROID_ID and other information to generate, generally one or more elements are used as device fingerprints;
- traditional iOS system device fingerprint extraction mainly relies on IDFA, IDFV, OpenUDID, etc. Value to generate.
- Google restricts the acquisition of IMEI, MAC and other values. The traditional fingerprint acquisition can no longer be used to uniquely identify the device, and the acquisition of this information requires permission applications that will significantly affect the user experience.
- the present invention proposes a set of device fingerprint extraction technology based on smart phone sensor information.
- an embodiment of the first aspect of the present invention proposes a device fingerprint extraction method based on a smartphone sensor, which includes the following steps:
- Step 1 Data collection
- Step 2 Data preprocessing
- Step 3 ADC value recovery
- Step 4 Estimation of the gain matrix
- Step 5 Validity check
- Step 6 Device fingerprint ID generation.
- an embodiment of the second aspect of the present invention proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor runs The computer program is executed to realize the method.
- an embodiment of the third aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the method.
- the invention has the advantages of being able to accurately extract device fingerprints, and when sampling some devices to restore factory settings, wipe machine upgrades, and time management tests, the device fingerprint ID does not change, and the device can be uniquely identified.
- Fig. 1 shows a flow chart of a method for extracting device fingerprints based on a smartphone sensor according to an embodiment of the present invention
- Fig. 2 shows an example diagram of data collected by a sensor according to an embodiment of the present invention
- FIG. 3 shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention
- Fig. 4 shows a schematic diagram of a computer medium provided by an embodiment of the present invention.
- first and second are used to distinguish different objects, rather than to describe a specific order.
- the terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment.
- the present invention uses the output of sensors such as gyroscopes, accelerometers, and magnetometers to infer the factory calibration data of each sensor, and uses these calibration data to construct a valid device fingerprint.
- the device fingerprint can effectively identify the uniqueness of the device.
- the present invention chooses to use sensor information because the acquisition of these data does not require any permission application, the effective acquisition of Android and iOS systems has commonality, and the effective value rate is relatively high, and the collection of sample data can be completed within one second.
- the extracted fingerprint information will not change with time and operations such as restoring factory settings, updating and upgrading.
- the present invention proves that the method of the present invention can generate a unique fingerprint for the device.
- the method adopted in the present invention is to infer the factory calibration data of each device based on the output of sensors such as gyroscopes, accelerometers, and magnetometers.
- MEMS sensors usually convert analog quantities into digital quantities through an analog-to-digital converter (ADC) and store them in registers.
- ADC analog-to-digital converter
- the sensor standard output can be expressed as:
- P i ⁇ P refers to the scale factor
- N ij ⁇ N refers to the non-orthogonality between the axes i and j
- B i ⁇ B refers to the standard deviation
- A refers to the sensitivity or gain of the sensor, defined as The ratio between the output signal and the measured property
- the nominal gain of the sensor is the expected operating sensitivity of the sensor.
- M is used to represent the nominal gain of the sensor. If the sensor is ideal, its scale matrix P and non-orthogonality matrix N should be M ⁇ I and I, respectively, where I is the identity matrix. So the above equation can be further simplified as:
- Sensor calibration standards have many latitudes, including high-precision equipment, multi-position, Kalman filter and vision-based, etc. Manufacturers generally choose to calibrate only the offset vector to reduce costs. After the factory calibration is completed, these calibration data will be written to the hardware storage. These data will not change over time and will not be easily rewritten.
- the gain matrix H and the offset matrix D should be unique, so any one of the two data that can be recovered can be used as the fingerprint of the device.
- the specific embodiment of the present invention describes the restoration of the gain matrix H.
- the present invention needs to know the nominal gain of each sensor device, and the nominal gain of the device is generally found in the manufacturer’s sensor device specifications.
- the nominal gain of the gyroscope of HUAWEI Mate 30 is 60mdps
- the iPhone X's gyroscope has a nominal gain of 61mdps
- the Google Pixel3XL's gyroscope has a nominal gain of 64mdps, and so on.
- the offset matrix D can be eliminated by the difference between the two sensors.
- the actual gain matrix and the ideal gain matrix are very similar, but not equal.
- the basic method for generating sensor fingerprints of the present invention includes six steps: data collection, data preprocessing, ADC value restoration, gain matrix estimation, validity check, and device fingerprint ID generation. Obtaining sensor fingerprints when the device is stationary or moving slowly can be more effective generation. In the following, the present invention takes the generation of fingerprints from gyroscope data as an example.
- Step 2 Data preprocessing
- ⁇ O [O 1 -O 0 , O 2 -O 1 ,..., O n-1 -O n-2 ]
- Step 3 ADC value recovery
- the present invention mainly restores ⁇ A, which is the difference between successive ADCs.
- H -1 is the inverse matrix of the actual gain matrix H, but the actual gain matrix is unknown here, so the present invention can temporarily replace the actual gain value with the nominal gain:
- H 0 M H ⁇ I
- M H is the gyroscope's Nominal gain, this value can be obtained from the manufacturer in the present invention.
- the present invention only takes the integer value of ⁇ A, which is estimated by the following equation:
- H 0 is not equal to H, this is just an estimate, so It is not really the data to be obtained, and the present invention also needs to eliminate invalid values through the following equation:
- the threshold can be set to 0.1.
- the number is selected according to the ADC value, and the last retained ADC value can be regarded as real data.
- the absolute value of ⁇ A is very small, only try to use it in a stationary or slow-moving state.
- Step 4 Estimation of the gain matrix
- the present invention can use the following equation to estimate the nominal gain
- the std here is the standard deviation function. If the error is small, then the It is very close to the real H. Take HUAWEI Mate 30 as an example, The values obtained at this time are as follows:
- the ADC resolution of the Mate30 device is 16 bits.
- the present invention can convert rds to dps and estimate the integer value:
- the unit is the value in dps.
- the error range of E obtained at this time can be 0.01. When the error is greater than this value, it means that the group of data is not available.
- the current device may be in a moving state. When this happens, the present invention needs to re-collect data to calculate , Until the error is small to a certain extent.
- Step 6 Device fingerprint ID generation
- the generation of fingerprint ID can be obtained according to the following formula:
- DeviceID is the gain matrix H after the nominal gain is extracted in the unit of dps.
- An example of the DeviceID of HWAWEI Mate 30 is as follows:
- the present invention uses the same scheme as the basic steps.
- the present invention does not directly use the ⁇ O input algorithm, but uses data in a different range to update the iteration H.
- the specific steps include the following:
- Step 2 Data preprocessing
- the present invention first generates data with a smaller value from ⁇ O, because the smaller value is unlikely to be outside the rounding error, so the present invention can sort the elements in ⁇ O, and then remove the difference between adjacent elements , The result value obtained in this way will be very small.
- the diff function uses different continuous vectors in the matrix.
- the calculation formula of diff( ⁇ O) is:
- Equation 1 By subtracting similar vectors, ⁇ O contains more data with smaller values. According to Equation 1, the present invention can obtain:
- the values of diff( ⁇ A) are all integers.
- the present invention can see that the value of H -1 ⁇ O should only contain integers. Therefore, the present invention can directly add ⁇ O to ⁇ O. Generate more data with smaller values. Then the present invention generates multiple batches of data from the expanded data according to the range of values, and updates them.
- the absolute values of all elements are lower than the product of the programmed gain, that is:
- Step 3 ADC value recovery
- Step 4 Estimation of the gain matrix
- This step is mainly update from each batch of data ⁇ O ⁇ i area
- the main difference between the improved method and the basic method is that the improved method can return to the step of data preprocessing after gain matrix estimation.
- the improvement method is to update iterations from a small range of data to the entire data set, and through the estimated To estimate the output value of the ADC, this reduces the error of each estimation, so even if the device is moving, it can generate DeviceID.
- the basic method can illustrate the feasibility of sensor fingerprints, and the improved method can be applied to actual scenes and is more reliable. Therefore, this is the preferred method for device fingerprint acquisition in actual situations.
- the std here is the standard deviation function. If the error is small, then the It is very close to the real H. Take HUAWEI Mate 30 as an example, The values obtained at this time are as follows:
- the ADC resolution of the Mate30 device is 16 bits.
- the present invention can convert rds to dps and estimate the integer value:
- the unit is the value in dps.
- the error range of E obtained at this time can be 0.01. When the error is greater than this value, it means that the group of data is not available.
- the current device may be in a moving state. When this happens, the present invention needs to re-collect data to calculate , Until the error is small to a certain extent.
- Step 6 Device fingerprint ID generation
- the generation of fingerprint ID can be obtained according to the following formula:
- DeviceID is the gain matrix H after the nominal gain is extracted in dps.
- An example of the DeviceID of HWAWEI Mate 30 is as follows:
- the present invention has been tested by real equipment, and different equipment manufacturers have lack of sensor calibration. For cheap and low-end smart phone manufacturers, they generally do not perform MEMS calibration verification, while some Android manufacturers only do some tests on the devices in some models of equipment. Calibration, such as HUAWEI Mate series, Google Pixel series, etc., and iOS devices have been factory calibrated for sensors as early as iPhone4 and iPad3. For devices that have been factory calibrated for the original sensor, the present invention can accurately extract the device fingerprint. According to the statistics of the invention, after testing 5680 Android devices and 4675 iOS devices, the device fingerprint ID can be accurately extracted. The rate is 100%, and some devices are sampled to restore factory settings, wipe machine upgrades, time management tests, etc., and the fingerprint ID of the device has not changed, which can uniquely identify the device.
- the embodiment of the present invention also provides an electronic device corresponding to the device fingerprint extraction method based on the smart phone sensor provided in the foregoing embodiment, so as to execute the above device fingerprint extraction method based on the smart phone sensor.
- the electronic device may be a mobile phone, Tablet computers, video cameras, etc., are not limited in the embodiment of the present invention.
- FIG. 3 shows a schematic diagram of an electronic device provided by some embodiments of the present invention.
- the electronic device 2 includes: a processor 200, a memory 201, a bus 202, and a communication interface 203.
- the processor 200, the communication interface 203, and the memory 201 are connected by the bus 202; the memory 201 stores There is a computer program that can run on the processor 200, and when the processor 200 runs the computer program, the method for extracting device fingerprints based on a smartphone sensor provided by any one of the foregoing embodiments of the present invention is executed.
- the memory 201 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
- RAM Random Access Memory
- non-volatile memory such as at least one disk memory.
- the communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
- the bus 202 may be an ISA bus, a PCI bus, an EISA bus, or the like.
- the bus can be divided into an address bus, a data bus, a control bus, and so on.
- the memory 201 is used to store a program, and the processor 200 executes the program after receiving an execution instruction.
- the method for extracting device fingerprints based on a smartphone sensor disclosed in any of the foregoing embodiments of the present invention may be applied In the processor 200, or implemented by the processor 200.
- the processor 200 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the foregoing method may be completed by an integrated logic circuit of hardware in the processor 200 or instructions in the form of software.
- the aforementioned processor 200 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), off-the-shelf programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
- DSP digital signal processor
- ASIC application specific integrated circuit
- FPGA off-the-shelf programmable gate array
- the methods, steps, and logical block diagrams disclosed in the embodiments of the present invention can be implemented or executed.
- the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
- the steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor.
- the software module can be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers.
- the storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and completes the steps of the foregoing method in combination with its hardware.
- the electronic device provided in the embodiment of the present invention and the device fingerprint extraction method based on the smart phone sensor provided in the embodiment of the present invention are based on the same inventive concept, and have the same beneficial effects as the method adopted, operated, or implemented.
- the embodiment of the present invention also provides a computer-readable medium corresponding to the device fingerprint extraction method based on the smartphone sensor provided in the foregoing embodiment.
- FIG. 4 shows the computer-readable storage medium as the optical disc 30, which A computer program (ie, a program product) is stored thereon, and when the computer program is run by a processor, it executes the device fingerprint extraction method based on a smartphone sensor provided by any of the foregoing embodiments.
- examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), and other types of random Access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media will not be repeated here.
- PRAM phase change memory
- SRAM static random access memory
- DRAM dynamic random access memory
- RAM random Access memory
- ROM read-only memory
- EEPROM electrically erasable programmable read-only memory
- flash memory or other optical and magnetic storage media will not be repeated here.
- the computer-readable storage medium provided by the foregoing embodiment of the present invention and the device fingerprint extraction method based on the smartphone sensor provided by the embodiment of the present invention are based on the same inventive concept, and have methods adopted, run, or implemented by the stored application program. The same beneficial effect.
- first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with “first” and “second” may explicitly or implicitly include at least one of the features. In the description of the present invention, “plurality” means at least two, such as two, three, etc., unless otherwise specifically defined.
- a "computer-readable medium” can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or device or in combination with these instruction execution systems, devices, or devices.
- computer readable media include the following: electrical connections (electronic devices) with one or more wiring, portable computer disk cases (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
- the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because it can be used, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable media if necessary. The program is processed in a way to obtain the program electronically and then stored in the computer memory.
- each part of the present invention can be implemented by hardware, software, firmware or a combination thereof.
- multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
- Discrete logic gate circuits with logic functions for data signals Logic circuit, application specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.
- a person of ordinary skill in the art can understand that all or part of the steps carried in the method of the foregoing embodiments can be implemented by a program instructing relevant hardware to complete.
- the program can be stored in a computer-readable storage medium, and the program can be stored in a computer-readable storage medium. When executed, it includes one of the steps of the method embodiment or a combination thereof.
- the functional units in the various embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module.
- the above-mentioned integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.
- the aforementioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Abstract
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Claims (12)
- 一种基于智能手机传感器的设备指纹提取方法,其特征在于,包括如下步骤:步骤一:数据采集;步骤二:数据预处理;步骤三:ADC值恢复;步骤四:增益矩阵的估算;步骤五:有效性检查;步骤六:设备指纹ID生成。
- 根据权利要求1所述的一种基于智能手机传感器的设备指纹提取方法,其特征在于,所述步骤一具体包括:采集陀螺仪传感器数据,用O=[O 0,O 1,...,O n-1]来标识单组组数数据,用O i=[O x,O y,O z] T来表示不同轴的获取。
- 根据权利要求2所述的一种基于智能手机传感器的设备指纹提取方法,其特征在于,所述步骤二具体包括:在得到所述陀螺仪传感器数据后,首先对所述数据进行差分计算,用以下等式得到ΔO:ΔO=[O 1-O 0,O 2-O 1,...,O n-1-O n-2]。
- 根据权利要求2所述的一种基于智能手机传感器的设备指纹提取方法,其特征在于,所述步骤二具体包括:在得到所述陀螺仪传感器数据后,将得到的数据表示为:O=[O 0,O 1,...,O n-1]其中O i=[O x,O y,O z] T表示单轴数据,首先对ΔO ix的值进行升序排序处理,[ΔO] x表示排序后的数组,同理对ΔO iy、ΔO iz的值进行排序,并输出结果[ΔO] y、[ΔO] z,然后计算如下:ΔΔO=[diff([ΔO] x)diff([ΔO] y)diff([O] z)] (3)其中diff(ΔO)的计算公式为:diff(ΔO)=[ΔO 1-ΔO 0,...,ΔO n-1-ΔO n-2]H -1diff(ΔO)=diff(ΔA)直接将ΔΔO添加到ΔO内,多次执行此拓展,以生成更多的较小值的数据,根据值的范围从拓展数据中生成多批数据,并进行更新,所有元素的绝对值都低于编程增益的乘积,也就是:以此批次进行计算,然后每个批次的值加倍,直至该批次与ΔO相同为止,然后逐步将ΔO εi进行计算,并将H的值往后更新。
- 一种电子设备,包括:存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序,其特征在于,所述处理器运行所述计算机程序时执行以实现如权利要求1-10任一项所述的方法。
- 一种计算机可读存储介质,其上存储有计算机程序,其特征在于,所述程序被处理器执行时实现如权利要求1-10中任一项所述的方法。
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US10097538B1 (en) * | 2017-08-12 | 2018-10-09 | Growpath, Inc. | User authentication systems and methods |
CN108712253A (zh) * | 2018-06-06 | 2018-10-26 | 北京美好人生伙伴信息技术有限公司 | 一种基于手机传感器指纹的伪造移动端识别方法及装置 |
CN109766855A (zh) * | 2019-01-16 | 2019-05-17 | 中国科学技术大学 | 一种移动智能设备传感器指纹识别方法 |
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