CN110443016B - Information leakage prevention method, electronic device and storage medium - Google Patents
Information leakage prevention method, electronic device and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 66
- 230000002265 prevention Effects 0.000 title claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 66
- 238000012795 verification Methods 0.000 claims abstract description 54
- 238000012545 processing Methods 0.000 claims abstract description 31
- 230000008859 change Effects 0.000 claims abstract description 9
- 238000007689 inspection Methods 0.000 claims description 31
- 230000008569 process Effects 0.000 claims description 24
- 238000001574 biopsy Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 3
- 238000001727 in vivo Methods 0.000 description 11
- 238000012549 training Methods 0.000 description 10
- 230000003287 optical effect Effects 0.000 description 7
- 238000012360 testing method Methods 0.000 description 6
- 230000004397 blinking Effects 0.000 description 4
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- 238000004891 communication Methods 0.000 description 3
- 210000000887 face Anatomy 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
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- G—PHYSICS
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- 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
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
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Abstract
The invention relates to data security, and discloses an information leakage prevention method, which comprises the following steps: receiving a checking instruction sent by a viewer, judging whether leakage prevention processing is required to be executed, if so, shooting a real-time image of the viewer, identifying a face area, performing living examination on the face area, and performing identity authentication on the viewer corresponding to the face area passing through the living examination; controlling the image acquisition unit to change the focal length and detecting whether a highlight region exists in front of the electronic device, if so, judging that a lens exists in front of the electronic device, and if not, detecting through the lens; and when the identity verification and the lens detection are simultaneously passed or the anti-leakage processing is judged not to be executed, displaying the information to be checked in the checking instruction. The invention also discloses an electronic device and a computer storage medium. By utilizing the invention, information can be prevented from being peeped and leaked, and the information security is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information leakage preventing method, an electronic device, and a computer readable storage medium.
Background
With the rapid progress of society, information communication between people is very large. Mobile terminals such as smart phones and the like are used as a tool for realizing communication in different places and in real time, and are becoming an indispensable tool in the work and life of people. The functions of the mobile terminal are more and more abundant, chat application software can be installed, email can be received and sent, related information can be viewed online, and the like. When people chat or view documents in places such as shops, subway stations and the like with large people flow, information displayed on a screen is inevitably peeped by other people. This may lead to privacy leakage for the user and lower security.
In addition, the existing smart phones and anti-photographing mechanisms of tablet devices have the following steps: 1. and (3) screen capturing detection, wherein if the screen capturing detection detects that the user acquires screen information through the shortcut key of the mobile phone, the information display is closed, and the screen capturing picture result is deleted. The method is a scheme of a software layer, and can not prevent equipment such as a second mobile phone, a camera and the like from shooting outside a screen. 2. And adding a watermark on the display page of the key information so as to trace back corresponding responsible persons of information leakage. This is also a post-hoc remedy, and cannot be prevented in real time at the time of information leakage.
Disclosure of Invention
In view of the foregoing, the present invention provides an information leakage prevention method, an electronic device, and a computer readable storage medium, which are mainly aimed at preventing information from being peeped and improving information security.
In order to achieve the above object, the present invention provides an information leakage preventing method, which is applicable to an electronic device with an image acquisition unit, and the method includes:
s1, receiving a checking instruction sent by a viewer, judging whether leakage prevention processing is needed to be executed on information to be checked in the checking instruction according to a preset judging rule, and if so, executing the step S2 and the step S3 respectively;
s2, controlling the image acquisition unit to shoot a real-time image of the viewer, identifying a face area from the real-time image, performing living examination on the face area based on the identified face area and a preset living examination rule, and performing identity authentication on the viewer corresponding to the face area passing the living examination;
s3, controlling the image acquisition unit to change the focal length in real time and detecting whether a highlight region exists in front of the electronic device, if so, judging that a lens exists in front of the electronic device, wherein the checking instruction does not pass through the lens detection, otherwise, the checking instruction passes through the lens detection; and
And S4, displaying the information to be checked in the checking instruction when the identity verification and the lens detection are simultaneously carried out or the anti-leakage processing is judged not to be required to be executed.
In addition, to achieve the above object, the present invention also provides an electronic device, including: the information leakage prevention program can be executed by the processor and can realize any step in the information leakage prevention method.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium including an information leakage prevention program which, when executed by a processor, can implement any of the steps in the information leakage prevention method described above.
According to the information leakage prevention method, the electronic device and the computer readable storage medium, through collecting the real-time image in front of the screen, the identity of a person in the real-time image is verified, the external lens is detected, the sensitive information needing to be kept secret is prevented from being checked by a person without checking authority, and the sensitive information is prevented from being shot by stealth; performing living examination on a human face area in a real-time image before identity verification, taking the change of the number of eyes in the same human face area as a living examination basis, simplifying living examination operation and improving the accuracy of living examination; in the process of detecting the external lens, the shape of the external lens is considered while the optical characteristics of the lens are utilized, so that the error detection of the lens is prevented, and the accuracy of detecting the external lens is improved; in conclusion, the information security is improved, and information is effectively prevented from being peeped and leaked in real time.
Drawings
FIG. 1 is a flow chart of a method for preventing information leakage according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of an electronic device according to a preferred embodiment of the invention;
FIG. 3 is a schematic diagram of a program module of the information leakage prevention program in FIG. 2 according to a preferred embodiment.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides an information leakage prevention method. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
Referring to fig. 1, a flowchart of a preferred embodiment of an information leakage prevention method according to the present invention is shown. The method is applicable to electronic devices with image acquisition units (e.g., front cameras).
In this embodiment, the method includes: step S1-step S4.
Step S1, receiving a checking instruction sent by a viewer, judging whether leakage prevention processing is needed to be executed on information to be checked in the checking instruction according to a preset judging rule, and if so, executing step S2 and step S3 respectively.
The viewing instruction comprises information to be viewed, such as documents, pictures, chat information and the like. In this embodiment, the preset determination rule includes:
Reading information to be checked in the checking instruction, and determining the confidentiality level of the information to be checked based on the mapping relation between the information to be checked and the confidentiality level; when the confidentiality level of the information to be checked meets the preset condition, judging that anti-leakage processing needs to be executed; otherwise, it is judged that the leak prevention processing is not required to be performed.
It should be noted that, the security level of the information to be checked needs to be predetermined and stored, and the white list corresponding to the data of each security level is determined.
In this embodiment, the security level of the information to be viewed includes: primary security, secondary security, tertiary security. Wherein, the first level of security indicates the highest security level, the third level of security indicates the lowest security level, and so on. The preset condition is that the confidentiality level is two or more.
And S2, controlling the image acquisition unit to shoot a real-time image of the viewer, identifying a face area from the real-time image, performing living examination on the face area based on the identified face area and a preset living examination rule, and performing identity authentication on the viewer corresponding to the face area passing the living examination.
In order to facilitate the living body examination, a plurality of real-time images are taken.
In this embodiment, the step of identifying a face region from the real-time image includes:
a1, performing integral graph operation on the real-time image, traversing the whole real-time image to be detected by using a sub-window with a preset size, and inputting the sub-image intercepted by the sub-window into a preset classifier to judge whether the sub-image contains face features or not;
a2, if judging that the sub-image contains the face feature, recording the position and the size of the sub-image, otherwise, discarding the current sub-image, and entering the detection of the next sub-image; and
and a3, amplifying the size of the sub-window after the sub-window traverses the whole real-time image, traversing the real-time image to be detected again until the size of the sub-window exceeds the image size of the real-time image, finishing detection, and determining all face areas in the real-time image.
For example, the size of the sub-window of the predetermined size may be, but is not limited to, 10×10. When the sub-window size is enlarged, it can be enlarged to 15×15, 20×20, 25×25, 30×30, etc. It will be appreciated that the size of the sub-windows may be adjusted as desired.
In this embodiment, the Haar-like features are used to represent the face features, and the concept of "integral map" is used for the fast computation of Haar rectangular features. The preset classifier is as follows: training by using an AdaBoost algorithm to obtain a large number of weak classifiers, cascading the weak classifiers according to a weight compensation mode to obtain strong classifiers, and connecting the strong classifiers obtained by training in series to form a cascading classifier. Through training a large number of face images and non-face images, a series of weak classifiers are obtained through multiple iterations, in the iteration process, the weak classifier with the minimum error rate is selected as the weak classifier hn (x) generated in the iteration process, then the sample weight is updated, so that samples which are misjudged in the last detection process can be paid enough attention to in the detection process, then the iteration is performed again, a new weak classifier is obtained, and the like, k weak classifiers are obtained through k iterations, and the strong classifier with strong classification capability is obtained after all the weak classifiers are combined. The strong classifiers have corresponding thresholds at each stage, all images containing human faces can smoothly pass through the classifier by adjusting the threshold of each stage of strong classifier, most of non-human face images are rejected, a large number of non-human face images can be filtered out by only a small number of features before the classifier is positioned, the structure of the classifier is relatively complex after the classifier is positioned, the non-human face images with certain human face features can be eliminated by more features, and finally, the human face images are reserved by detection of each stage of strong classifier, and the non-human face images are eliminated by a certain stage of strong classifier in the detection process.
Further, the step of identifying a face region from the real-time image further includes: and screening the face region to be analyzed meeting the preset conditions from the identified face region, and storing according to the preset size.
It should be noted that, in a real-time image, a plurality of face regions may be identified, in this case, a face region having a size satisfying a preset condition (for example, 30×30 or more) is selected as the face region to be analyzed, and the face region is stored according to a preset size. One or more face regions to be analyzed may be identified in a real-time image.
In order to further improve the efficiency, the step of identifying the face region from the real-time image further includes: and performing base64 coding processing on the face region to be analyzed. By carrying out base64 coding processing on the face region, the operation efficiency can be improved, the data can be encrypted to a certain extent, and the safety of the data is improved.
In this embodiment, the step of performing the biopsy on the face area based on the identified face area and a preset biopsy rule includes:
respectively acquiring a human face region i in each real-time image, determining a human eye region corresponding to the human face region i in each real-time image, and detecting the number of eyes in the human eye region; and
Judging whether the number of eyes in the face area i changes within a preset time, if so, passing the living body inspection, otherwise, not passing the living body inspection.
It will be appreciated that normal individuals blink 10 to 20 times per minute, each blink being about 0.2 to 0.4 seconds. In capturing a real-time image, the front camera is controlled to capture a real-time image every m seconds (e.g., 0.3 seconds) for k seconds (e.g., 5 seconds).
Assuming that N real-time images are currently photographed, one or more face regions to be analyzed are sequentially identified from the N real-time images, and a living body test is required for each face region. Taking a face region i as an example, extracting the face region i from N real-time images respectively, determining human eye regions in the N face regions i respectively, counting the number of eyes in the N face regions i, and recording the number of eyes in the face region i in the N real-time images in an array mode. If one of the data j in the array is greater than 0, the subsequent data (j+1) is equal to 0, and the subsequent data (j+2) is greater than 0, the blinking operation may be defaulted to a blinking operation, that is, the human living body corresponding to the face region i, and the human living body test is determined to pass.
Repeating the above actions, and performing living body examination on the face area i+1 in the real-time image until all the face areas are subjected to living body examination.
Further, step S2 further includes:
and refusing to display the information to be checked when the face areas in the real-time images do not pass the living body inspection, and prompting a viewer to acquire the real-time images again.
Reasons why all face regions in the real-time image pass the in-vivo inspection may include: 1. the viewer holds the photo for identity verification in order to pass the identity verification; 2. when a real-time image of a viewer is taken, the viewer does not blink for a preset time, resulting in a failed live examination. In order to ensure the safety of the information, early warning information is sent out to prompt a viewer to acquire a real-time image again and carry out living body inspection again.
In this embodiment, the step of authenticating the viewer corresponding to the face area passing the living body inspection includes:
extracting feature data from the face region that passed the in-vivo test;
respectively calculating the similarity between the extracted characteristic data and characteristic data prestored in a preset database; and
And when the maximum value of the similarity is larger than or equal to a preset threshold value, judging that the face area passes the identity verification, otherwise, judging that the identity verification fails.
The preset database contains the feature data of the white list user with the viewing authority, the corresponding feature data can be found to prove that the current user has the viewing authority, namely, the identity verification is passed, and the user identity of the feature data corresponding to the corresponding similarity maximum value is used as the user identity of the face area.
In order to improve the accuracy of the identity verification, if the highest similarity value is smaller than a preset threshold value, judging that the user identity corresponding to the current face area cannot be confirmed, namely, the identity verification fails and the user identity does not have viewing authority.
Repeating the steps, and sequentially carrying out identity verification on other face areas which pass through living body detection.
It should be noted that, the preset database may also be a face recognition database of the public security department, and the user identity corresponding to the feature data is taken as the user identity corresponding to the current face region, and is matched with the user identity in the preset white list, if the matching is successful, the user identity has the viewing authority, otherwise, the user identity does not have the authority.
Further, the step of authenticating the viewer corresponding to the face region passing the living body inspection further includes:
and refusing to display the information to be checked when judging that the face areas passing through the living body inspection do not pass the identity verification, and prompting a viewer to acquire a real-time image again.
Reasons why the face regions that pass the in vivo inspection are all authenticated may include: the viewer does not have viewing rights itself, resulting in authentication failure. In order to ensure the safety of the information, early warning information is sent out to prompt a viewer to acquire a real-time image again and perform identity verification again.
The method mainly aims at judging whether a viewer in front of the mobile terminal has relevant rights or not so as to improve the safety of information and prevent information leakage.
And step S3, controlling the image acquisition unit to change the focal length in real time and detecting whether a highlight region exists in front of the electronic device, if so, judging that a lens exists in front of the electronic device, wherein the checking instruction does not pass through the lens detection, otherwise, the checking instruction passes through the lens detection.
In this embodiment, the optical characteristics of the lens are used to detect, and whether the highlight region is detected is used as a judgment basis to judge whether an external lens exists in front of the mobile terminal, so as to prevent information leakage. It will be appreciated that all lenses are focal in nature, projecting a front object of the screen with structured light in the front camera array of the handset, and by varying the focal length, the external lens surface will reflect high light when the focal point coincides with the focal point of the external lens.
Before displaying the information to be checked, the focal length of the lens is controlled to be continuously and dynamically changed (to be enlarged to a maximum focal length (generally 2 meters) and to be reduced to a minimum focal length such as 5 mm), and the lens is circulated once every preset time (for example, 3 seconds), if a high-light lens area is detected in one circulation, the existence of the lens is determined, namely, the lens is judged not to be detected.
In order to improve accuracy of lens detection, the step S3 further includes:
when a highlight region exists in front of an electronic device, acquiring an image corresponding to the highlight region, inputting the image corresponding to the highlight region into a preset lens identification model, and judging whether a lens exists in the image corresponding to the highlight region according to a model output result; and
If the model output result is that the lens exists, determining that an external lens exists in front of the electronic device; if the model output result is that no lens exists, judging that no external lens exists in the front of the electronic device.
The lens recognition model can be obtained by training a FAST-CNN model. The training process of the shot recognition model and the technique of recognizing shots from images by using the model are well established, and will not be described in detail here. Only when the model is satisfied to identify the lens from the image and a highlight region exists in the zooming process, the external lens is identified to exist, so that misjudgment of the lens is prevented, and the use experience of a viewer is influenced.
The steps S2 and S3 may be performed simultaneously without sequencing.
And S4, displaying the information to be checked in the checking instruction when the identity verification and the lens detection are simultaneously passed or the anti-leakage processing is judged not to be executed.
Under the condition of detection through a lens, when only one viewer is in the real-time image, the information to be checked can be displayed through the in-vivo inspection and the identity verification party; when a plurality of viewers exist in the real-time image, the display condition can be set to be that the viewers pass through identity verification at the same time, and at least one viewer can display information to be checked through the living body inspection party, so that the information to be checked is shielded as long as one viewer does not have the checking authority.
According to the information leakage prevention method provided by the embodiment, through collecting the real-time image in front of the screen, carrying out identity verification on people in the real-time image and carrying out external lens detection, sensitive information needing to be kept secret is prevented from being checked by people without checking authority, and leakage of the sensitive information caused by taking a photograph by mistake is prevented; performing living examination on a human face area in a real-time image before identity verification, taking the change of the number of eyes in the same human face area as a living examination basis, simplifying living examination operation and improving the accuracy of living examination; in the process of detecting the external lens, the shape of the external lens is considered while the optical characteristics of the lens are utilized, so that the error detection of the lens is prevented, and the accuracy of detecting the external lens is improved; in conclusion, the information security is improved, and information is effectively prevented from being peeped and leaked in real time.
Further, to prevent information from being captured by hand during the data presentation process, in other embodiments, the method further comprises:
and S5, detecting whether a lens exists in front of the electronic device in real time or at fixed time in the display process of the information to be checked, stopping displaying the information to be checked if the lens is detected, generating early warning information and continuously executing lens detection.
And when the sensitive information to be checked is displayed, detecting a highlight area in front of the electronic device in real time (or at fixed time, for example, 3 seconds and 1 time), determining whether an external lens exists in front of the electronic device, if so, generating early warning information and stopping displaying the information to be checked, otherwise, continuously displaying the information to be checked and continuously executing detection operation. Wherein, stopping the presentation may include: occlusion of the information to be viewed (e.g., blurring of sensitive information), or exit from the current interface. It should be noted that the lens detection step is the same as the embodiment of step S3, and is not described here again.
In other embodiments, the corresponding anti-leakage operation may also be determined according to the security level of the information to be checked. For example, when the information to be checked satisfies a first preset condition (the security level is two levels), it is determined that the first anti-leakage process (in-vivo inspection and authentication) needs to be performed; when the information to be checked meets a second preset condition (the security level is one level), the second anti-leakage processing (living body inspection, identity verification and lens detection) is judged to be required to be executed. The corresponding anti-leakage processing is preset for the information to be checked with different security levels, so that the information security is ensured, the verification step is simplified as much as possible, and the use experience of a user is improved.
It should be noted that all the data analysis operations (in vivo inspection, authentication, and lens detection) may be performed independently by the electronic device, in which case an information leakage prevention program is stored in the electronic device; the data to be analyzed can be sent to the server through the electronic device, and the subsequent operation is executed by receiving the signal fed back by the server, in which case an information leakage prevention program is required to be stored in the server, for example, a viewer sends a viewing instruction through the electronic device (for example, a mobile terminal), and the electronic device feeds the instruction back to the server; the server analyzes whether the information to be checked needs to execute anti-leakage operation, if so, the image acquisition unit (for example, a front camera of the mobile terminal) is controlled to acquire a real-time image, the real-time image is acquired, in-vivo inspection and identity verification are carried out on the real-time image to obtain a verification result, and meanwhile, the image acquisition unit is controlled to zoom and detect whether a highlight area exists or not so as to detect an external lens to obtain a detection result; based on the identity verification result and the lens detection result, feeding back corresponding instructions (showing information to be checked or refusing to show, etc.) to the electronic device; and the electronic device executes corresponding operation after receiving the instruction fed back by the server. The specific implementation method is substantially the same as that in each of the above method embodiments, and will not be described herein.
The invention further provides an electronic device. Referring to fig. 2, a schematic diagram of a preferred embodiment of an electronic device according to the present invention is shown.
In this embodiment, the electronic apparatus 1 may be a terminal device with a data processing function, such as a smart phone, a tablet computer, a portable computer, or a desktop computer, and the electronic apparatus 1 is provided with an image capturing unit for capturing an image in front of the electronic apparatus 1.
The electronic device 1 comprises a memory 11, a processor 12, an image acquisition unit 13 and a display unit 14.
The memory 11 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic apparatus 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic apparatus 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus 1.
The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as the information leakage prevention program 10, but also for temporarily storing data that has been output or is to be output.
The processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as the information leakage prevention program 10, etc.
The image capturing unit 13 may be a camera unit, such as a smart phone, a tablet computer, a front camera of a portable computer, an external camera of a desktop computer, etc.
The display unit 14 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch device, or the like. The display unit may also be referred to as a display screen or a display, for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
Fig. 2 shows only the electronic device 1 with the components 11-14, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or a different arrangement of components.
Optionally, the electronic device 1 may also comprise a communication unit, e.g. a Wi-Fi unit, a SIM (Subscriber Identification Module) card based mobile communication unit, etc.
Alternatively, the electronic device 1 may further include a light emitting unit, for example, a flash or the like.
In the embodiment of the electronic device 1 shown in fig. 2, as a program code of the information leakage preventing program 10 stored in the memory 11 of a computer storage medium, the processor 12 executes the program code of the information leakage preventing program 10 to realize the steps of:
an instruction receiving step: receiving a checking instruction sent by a viewer, judging whether leakage prevention processing is needed to be executed on information to be checked in the checking instruction according to a preset judging rule, and if so, executing an identity verification step and a lens detection step.
Taking the electronic device 1 as an example of a mobile terminal, a viewer sends a viewing instruction through the mobile terminal, where the viewing instruction includes information to be viewed, such as a document, a picture, chat information, and the like. In this embodiment, the preset determination rule includes:
reading information to be checked in the checking instruction, and determining the confidentiality level of the information to be checked based on the mapping relation between the information to be checked and the confidentiality level; when the confidentiality level of the information to be checked meets the preset condition, judging that anti-leakage processing needs to be executed; otherwise, it is judged that the leak prevention processing is not required to be performed.
It should be noted that, the security level of the information to be checked needs to be predetermined and stored, and the white list corresponding to the data of each security level is determined.
In this embodiment, the security level of the information to be viewed includes: primary security, secondary security, tertiary security. Wherein, the first level of security indicates the highest security level, the third level of security indicates the lowest security level, and so on. The preset condition is that the confidentiality level is two or more.
And an identity verification step of controlling the image acquisition unit 13 to shoot a real-time image, identifying a face area from the real-time image, performing a living body verification on the face area based on the identified face area and a preset living body verification rule, and performing identity verification on a viewer corresponding to the face area passing the living body verification.
In order to facilitate the living body examination, a plurality of real-time images are taken.
In this embodiment, the step of identifying a face region from the real-time image includes:
a1, performing integral graph operation on the real-time image, traversing the whole real-time image to be detected by using a sub-window with a preset size, and inputting the sub-image intercepted by the sub-window into a preset classifier to judge whether the sub-image contains face features or not;
A2, if judging that the sub-image contains the face feature, recording the position and the size of the sub-image, otherwise, discarding the current sub-image, and entering the detection of the next sub-image; and
and a3, amplifying the size of the sub-window after the sub-window traverses the whole real-time image, traversing the real-time image to be detected again until the size of the sub-window exceeds the image size of the real-time image, finishing detection, and determining all face areas in the real-time image.
For example, the size of the sub-window of the predetermined size may be, but is not limited to, 10×10. When the sub-window size is enlarged, it can be enlarged to 15×15, 20×20, 25×25, 30×30, etc. It will be appreciated that the size of the sub-windows may be adjusted as desired.
In this embodiment, the Haar-like features are used to represent the face features, and the concept of "integral map" is used for the fast computation of Haar rectangular features. The preset classifier is as follows: training by using an AdaBoost algorithm to obtain a large number of weak classifiers, cascading the weak classifiers according to a weight compensation mode to obtain strong classifiers, and connecting the strong classifiers obtained by training in series to form a cascading classifier. Through training a large number of face images and non-face images, a series of weak classifiers are obtained through multiple iterations, in the iteration process, the weak classifier with the minimum error rate is selected as the weak classifier hn (x) generated in the iteration process, then the sample weight is updated, so that samples which are misjudged in the last detection process can be paid enough attention to in the detection process, then the iteration is performed again, a new weak classifier is obtained, and the like, k weak classifiers are obtained through k iterations, and the strong classifier with strong classification capability is obtained after all the weak classifiers are combined. The strong classifiers have corresponding thresholds at each stage, all images containing human faces can smoothly pass through the classifier by adjusting the threshold of each stage of strong classifier, most of non-human face images are rejected, a large number of non-human face images can be filtered out by only a small number of features before the classifier is positioned, the structure of the classifier is relatively complex after the classifier is positioned, the non-human face images with certain human face features can be eliminated by more features, and finally, the human face images are reserved by detection of each stage of strong classifier, and the non-human face images are eliminated by a certain stage of strong classifier in the detection process.
Further, the step of identifying a face region from the real-time image further includes: and screening the face region to be analyzed meeting the preset conditions from the identified face region, and storing according to the preset size.
It should be noted that, in a real-time image, a plurality of face regions may be identified, in this case, a face region having a size satisfying a preset condition (for example, 30×30 or more) is selected as the face region to be analyzed, and the face region is stored according to a preset size. One or more face regions to be analyzed may be identified in a real-time image.
In order to further improve the efficiency, the step of identifying the face region from the real-time image further includes: and performing base64 coding processing on the face region to be analyzed. By carrying out base64 coding processing on the face region, the operation efficiency can be improved, the data can be encrypted to a certain extent, and the safety of the data is improved.
In this embodiment, the step of performing the biopsy on the face area based on the identified face area and a preset biopsy rule includes:
respectively acquiring a human face region i in each real-time image, determining a human eye region corresponding to the human face region i in each real-time image, and detecting the number of eyes in the human eye region; and
Judging whether the number of eyes in the face area i changes within a preset time, if so, passing the living body inspection, otherwise, not passing the living body inspection.
It will be appreciated that normal individuals blink 10 to 20 times per minute, each blink being about 0.2 to 0.4 seconds. In capturing a real-time image, the front camera is controlled to capture a real-time image every m seconds (e.g., 0.3 seconds) for k seconds (e.g., 5 seconds).
Assuming that N real-time images are currently photographed, one or more face regions to be analyzed are sequentially identified from the N real-time images, and a living body test is required for each face region. Taking a face region i as an example, extracting the face region i from N real-time images respectively, determining human eye regions in the N face regions i respectively, counting the number of eyes in the N face regions i, and recording the number of eyes in the face region i in the N real-time images in an array mode. If one of the data j in the array is greater than 0, the subsequent data (j+1) is equal to 0, and the subsequent data (j+2) is greater than 0, the blinking operation may be defaulted to a blinking operation, that is, the human living body corresponding to the face region i, and the human living body test is determined to pass.
Repeating the above actions, and performing living body examination on the face area i+1 in the real-time image until all the face areas are subjected to living body examination.
Further, the step of authenticating further comprises:
and refusing to display the information to be checked when the face areas in the real-time images do not pass the living body inspection, and prompting a viewer to acquire the real-time images again.
Reasons why all face regions in the real-time image pass the in-vivo inspection may include: 1. the viewer holds the photo for identity verification in order to pass the identity verification; 2. when a real-time image of a viewer is taken, the viewer does not blink for a preset time, resulting in a failed live examination. In order to ensure the safety of the information, early warning information is sent out to prompt a viewer to acquire a real-time image again and carry out living body inspection again.
In this embodiment, the step of authenticating the viewer corresponding to the face area passing the living body inspection includes:
extracting feature data from the face region that passed the in-vivo test;
respectively calculating the similarity between the extracted characteristic data and characteristic data prestored in a preset database; and
And when the maximum value of the similarity is larger than or equal to a preset threshold value, judging that the face area passes the identity verification, otherwise, judging that the identity verification fails.
The preset database contains the feature data of the white list user with the viewing authority, the corresponding feature data can be found to prove that the current user has the viewing authority, namely, the identity verification is passed, and the user identity of the feature data corresponding to the corresponding similarity maximum value is used as the user identity of the face area.
In order to improve the accuracy of the identity verification, if the highest similarity value is smaller than a preset threshold value, judging that the user identity corresponding to the current face area cannot be confirmed, namely, the identity verification fails and the user identity does not have viewing authority.
Repeating the steps, and sequentially carrying out identity verification on other face areas which pass through living body detection.
It should be noted that, the preset database may also be a face recognition database of the public security department, and the user identity corresponding to the feature data is taken as the user identity corresponding to the current face region, and is matched with the user identity in the preset white list, if the matching is successful, the user identity has the viewing authority, otherwise, the user identity does not have the authority.
Further, the step of authenticating the viewer corresponding to the face region passing the living body inspection further includes:
and refusing to display the information to be checked when judging that the face areas passing through the living body inspection do not pass the identity verification, and prompting a viewer to acquire a real-time image again.
Reasons why the face regions that pass the in vivo inspection are all authenticated may include: the viewer does not have viewing rights itself, resulting in authentication failure. In order to ensure the safety of the information, early warning information is sent out to prompt a viewer to acquire a real-time image again and perform identity verification again.
The method mainly aims at judging whether a viewer in front of the mobile terminal has relevant rights or not so as to improve the safety of information and prevent information leakage.
And a lens detection step: the image acquisition unit 13 is controlled to change the focal length in real time and detect whether a highlight region exists in front of the electronic device 1, if the highlight region exists, the viewing instruction is judged to not pass through the lens detection, and if not, the viewing instruction passes through the lens detection.
In this embodiment, the optical flow method and the optical characteristics of the lens are used to detect, and whether the highlight region is detected is used as a judgment basis to judge whether an external lens exists in front of the mobile terminal, so as to prevent information leakage. It will be appreciated that all lenses are focal in nature, projecting a front object of the screen with structured light in the front camera array of the handset, and by varying the focal length, the external lens surface will reflect high light when the focal point coincides with the focal point of the external lens.
Before displaying the information to be checked, the focal length of the lens is controlled to be continuously and dynamically changed (to be enlarged to a maximum focal length (generally 2 meters) and to be reduced to a minimum focal length such as 5 mm), and the lens is circulated once every preset time (for example, 3 seconds), if a high-light lens area is detected in one circulation, the existence of the lens is determined, namely, the lens is judged not to be detected.
In order to improve accuracy of lens detection, the step S3 further includes:
when a highlight region exists in front of the electronic device 1, acquiring an image corresponding to the highlight region, inputting the image corresponding to the highlight region into a preset lens identification model, and judging whether a lens exists in the image corresponding to the highlight region according to a model output result; and
If the model output result is that the lens exists, determining that an external lens exists in front of the electronic device 1; if the model output result is that no lens exists, it is determined that no external lens exists in the electronic device 1.
The lens recognition model can be obtained by training different models (for example, a FAST-CNN model). The training process of the shot recognition model and the technique of recognizing shots from images by using the model are well established, and will not be described in detail here. Only when the model is satisfied to identify the lens from the image and a highlight region exists in the zooming process, the external lens is identified to exist, so that misjudgment of the lens is prevented, and the use experience of a viewer is influenced.
It should be noted that the authentication step and the lens detection step may be performed simultaneously, regardless of the sequence.
Information display step: and when the identity verification and the lens detection are simultaneously carried out or the anti-leakage processing is judged not to be needed, displaying the information to be checked in the checking instruction.
Under the condition of detection through a lens, when only one viewer is in the real-time image, the information to be checked can be displayed through the in-vivo inspection and the identity verification party; when a plurality of viewers exist in the real-time image, the display condition can be set to be that the viewers pass through identity verification at the same time, and at least one viewer can display information to be checked through the living body inspection party, so that the information to be checked is shielded as long as one viewer does not have the checking authority.
In other embodiments, when the processor 12 executes the program code of the information leakage prevention program 10, the following steps are also implemented: and detecting whether a lens exists in front of the electronic device in real time or at fixed time in the display process of the information to be checked, and stopping displaying the information to be checked if the lens is detected, generating early warning information and continuously executing lens detection.
And when the sensitive information to be checked is displayed, detecting a highlight area in front of a screen of the mobile terminal in real time (or at fixed time, for example, 3 seconds and 1 time), determining whether an external lens exists in front of the mobile terminal, if so, generating early warning information and stopping displaying the information to be checked, otherwise, continuously displaying the information to be checked and continuously executing detection operation. Wherein, stopping the presentation may include: occlusion of the information to be viewed (e.g., blurring of sensitive information), or exit from the current interface.
Alternatively, in other embodiments, the information leakage prevention program 10 may be divided into one or more modules, one or more modules being stored in the memory 11 and executed by the one or more processors 12 to perform the present invention, where a module refers to a series of computer program instruction segments capable of performing a specific function.
For example, referring to fig. 3, which is a schematic diagram of a program module of the preferred embodiment of the information leakage prevention program 10 in fig. 2, in this embodiment, the information leakage prevention program 10 may be divided into a receiving module 110, an authentication module 120, a lens detection module 130 and a presentation module 140, where the functions or operation steps implemented by the modules 110-140 are similar to those described above, and are not described in detail herein, for example:
The receiving module 110 is configured to receive a viewing instruction sent by a viewer, and determine whether leakage prevention processing needs to be performed on information to be viewed in the viewing instruction according to a preset determination rule;
the identity verification module 120 is configured to control the image acquisition unit 13 to capture a real-time image, identify a face region from the real-time image, perform a living body verification on the face region based on the identified face region and a preset living body verification rule, and perform an identity verification on a viewer corresponding to the face region that passes the living body verification;
the lens detection module 130 is configured to control the image acquisition unit 13 to change the focal length in real time and detect whether a highlight region exists in front of the electronic device 1, if so, determine that an external lens exists in front of the electronic device 1, and not pass the lens detection, otherwise, pass the lens detection; and
And the display module 140 is configured to display information to be checked in the checking instruction when the authentication and the lens detection are passed at the same time, or when it is determined that the anti-leakage processing is not required to be performed.
In addition, an embodiment of the present invention also proposes a computer-readable storage medium, in which an information leakage prevention program 10 is included, the information leakage prevention program 10 implementing the following operations when executed by a processor:
Receiving a checking instruction sent by a viewer, and judging whether leakage prevention processing is required to be executed on information to be checked in the checking instruction according to a preset judging rule;
controlling the image acquisition unit to shoot a real-time image of the viewer, identifying a face area from the real-time image, performing living detection on the face area based on the identified face area and a preset living detection rule, and performing identity verification on the viewer corresponding to the face area passing through living detection;
the image acquisition unit 13 is controlled to change the focal length in real time and detect whether a highlight region exists in front of the electronic device 1, if the highlight region exists, the checking instruction does not pass through the lens detection if the lens exists in front of the electronic device 1, otherwise, the checking instruction passes through the lens detection; and
And when the identity verification and the lens detection are simultaneously carried out or the anti-leakage processing is judged not to be needed, displaying the information to be checked in the checking instruction.
The embodiment of the computer readable storage medium of the present invention is substantially the same as the embodiment of the above-mentioned information leakage prevention method, and will not be described herein.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description of the preferred embodiments of the present invention should not be taken as limiting the scope of the invention, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the following description and drawings, or by direct or indirect application to other relevant art(s).
Claims (9)
1. An information leakage prevention method is suitable for an electronic device with an image acquisition unit, and is characterized by comprising the following steps:
an instruction receiving step: receiving a checking instruction sent by a viewer, determining the security level of the information to be checked according to the mapping relation between the security level of the information to be checked and a white list corresponding to the security level, and judging whether leakage prevention processing is required to be executed on the information to be checked in the checking instruction according to the security level of the information to be checked, wherein the leakage prevention processing comprises: the method comprises the steps of identity verification and lens detection, if yes, an identity verification step and a lens detection step are respectively executed;
and (3) identity verification: controlling the image acquisition unit to shoot a real-time image of the viewer, identifying a face area from the real-time image, performing living examination on the face area based on the identified face area and a preset living examination rule, extracting feature data from the face area passing the living examination, respectively calculating the similarity between the extracted feature data and feature data of a white list user with viewing authority pre-stored in a preset database, judging that the face area passes the identity authentication when the maximum value of the similarity is larger than or equal to a preset threshold value, and otherwise judging that the identity authentication fails;
And a lens detection step: the image acquisition unit is controlled to change the focal length in real time, whether a highlight area exists in front of the electronic device is detected, if the highlight area exists, the situation that a lens exists in front of the electronic device is judged, the checking instruction does not pass through the lens detection, otherwise, the checking instruction passes through the lens detection; and
Information display step: and when the identity verification and the lens detection are simultaneously carried out or the anti-leakage processing is judged not to be needed, displaying the information to be checked in the checking instruction.
2. The information leakage prevention method according to claim 1, wherein the step of identifying a face region from the real-time image comprises:
a1, performing integral graph operation on the real-time image, traversing the whole real-time image to be detected by using a sub-window with a preset size, and inputting the sub-image intercepted by the sub-window into a preset classifier to judge whether the sub-image contains face features or not;
a2, if judging that the sub-image contains the face feature, recording the position and the size of the sub-image, otherwise, discarding the current sub-image, and entering the detection of the next sub-image; and
and a3, amplifying the size of the sub-window after the sub-window traverses the whole real-time image, traversing the real-time image to be detected again until the size of the sub-window exceeds the image size of the real-time image, finishing detection, and determining all face areas in the real-time image.
3. The information leakage prevention method according to claim 2, wherein the step of performing a biopsy of the face region based on the identified face region and a preset biopsy rule comprises:
respectively acquiring a human face region i in each real-time image, determining a human eye region corresponding to the human face region i in each real-time image, and detecting the number of eyes in the human eye region; and
Judging whether the number of eyes in the face area i changes within a preset time, if so, passing the living body inspection, otherwise, not passing the living body inspection.
4. The information leakage prevention method according to claim 3, wherein the authentication step further comprises:
and refusing to display the information to be checked when the face areas in the real-time images do not pass the living body inspection, and prompting a viewer to acquire the real-time images again.
5. The information leakage prevention method according to claim 1, wherein the step of authenticating the viewer corresponding to the face area passing the living body inspection further comprises:
and refusing to display the information to be checked when judging that the face areas passing through the living body inspection do not pass the identity verification, and prompting a viewer to acquire a real-time image again.
6. The information leakage prevention method according to claim 2, wherein the lens detection step further comprises:
when judging that a highlight region exists in front of the electronic device, acquiring an image corresponding to the highlight region, inputting the image corresponding to the highlight region into a preset lens identification model, and judging whether a lens exists in the image corresponding to the highlight region according to a model output result.
7. The information leakage prevention method according to any one of claims 1 to 6, further comprising:
and detecting whether a lens exists in front of the electronic device in real time or at fixed time in the display process of the information to be checked, and stopping displaying the information to be checked if the lens is detected, generating early warning information and continuously executing lens detection.
8. An electronic device comprising a memory, a processor and an image acquisition unit, wherein an information leakage prevention program capable of running on the processor is stored in the memory, and the information leakage prevention program can implement the steps of the information leakage prevention method according to any one of claims 1 to 7 when being executed by the processor.
9. A computer-readable storage medium, wherein an information leakage prevention program is included in the computer-readable storage medium, the information leakage prevention program, when executed by a processor, implementing the steps in the information leakage prevention method according to any one of claims 1 to 7.
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