CN108491785B - Artificial intelligence image identification attack defense system - Google Patents
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- CN108491785B CN108491785B CN201810223172.3A CN201810223172A CN108491785B CN 108491785 B CN108491785 B CN 108491785B CN 201810223172 A CN201810223172 A CN 201810223172A CN 108491785 B CN108491785 B CN 108491785B
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- 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
- G06V40/172—Classification, e.g. identification
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
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- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
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Abstract
The invention discloses an artificial intelligence image identification attack defense system, which comprises: the system comprises a monitor end and a server end, wherein the monitor end is in communication connection with the server end; the monitor end comprises an image target position detection module, an image digitization module, an image security coding module and a coding transmission encryption module, and the server end comprises a coding transmission decryption module, an image security decoding module, a numerical imaging module, an image target secondary classification algorithm module, an artificial intelligent deep learning server host and a background image target database; the invention carries out intelligent recognition of the human face through the artificial intelligent image recognition attack defense system, avoids the confusion attack of artificial intelligent image recognition, quickly judges the authenticity of the image target and improves the safety of human face recognition.
Description
Technical Field
The invention relates to the technical field of artificial intelligence image recognition, in particular to an artificial intelligence image recognition attack defense system.
Background
At present, the arrival of AI (Artificial Intelligence, a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human Intelligence) will lead human beings to enter a new era, and along with the development of computer technology and information technology, AI Artificial Intelligence increasingly affects our daily life.
The image recognition refers to a technology of processing, analyzing and understanding images by using a computer to recognize various targets and objects in different modes, and in general industrial use, an industrial camera is adopted to shoot pictures, and then software is used for further recognition processing according to the gray level difference of the pictures.
At present, the face recognition is applied in a large range, but the work in the aspect of safety protection is weak, particularly the front end (camera) of image acquisition, the safety protection capability is weak, and the situations of invasion and hijacking of the camera are very common; if an attacker invades the camera and tampers with the image and the video acquired by the camera, artificial intelligent processing such as background face recognition can be misled.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention aims to solve the technical problem that the artificial intelligent image identification attack defense system aims to perform intelligent identification on a human face through the artificial intelligent image identification attack defense system, avoid the confusion attack of artificial intelligent image identification, quickly judge the authenticity of an image target and improve the safety of human face identification.
The technical scheme adopted by the invention for solving the technical problem is as follows:
an artificial intelligence image recognition attack defense system, wherein the artificial intelligence image recognition attack defense system comprises:
the system comprises a monitor end and a server end, wherein the monitor end is in communication connection with the server end;
the monitor side includes:
the image target position detection module is used for identifying the target position of the image acquired by the monitor in real time and extracting pixel points needing security coding protection;
the image digitization module is used for carrying out digitization processing on the image;
the image security coding module is used for coding the digitized image by a security algorithm;
the encoding transmission encryption module is used for encrypting the encoding information and transmitting the encrypted encoding information to the server end through network packets;
the server side includes:
the coding transmission decryption module is used for decrypting the received coding information;
the image security decoding module is used for decoding the decrypted coding information into a numerical value before the original image is coded and restoring the numerical value into an original target image through a digital image algorithm;
the numerical value imaging module is used for carrying out imaging processing on the numerical value;
the image target classification algorithm module is used for preliminarily judging the authenticity of the image target through a rule base artificially defined by the authenticity characteristics of the target;
the artificial intelligent deep learning server host is used for comparing and identifying the detailed characteristics of the image target and then transmitting the characteristic information to the background image target database;
and the background image target database is used for returning or displaying the real data information of the image target according to the application range after comparing the corresponding information of the image target characteristics.
The artificial intelligence image recognition attack defense system, wherein, the monitor end further comprises: a monitor for image capture.
The artificial intelligence image recognition attack defense system is characterized in that the monitor comprises a camera and a mobile terminal.
The artificial intelligence image identification attack defense system is characterized in that the coding transmission encryption module encrypts coding information through a Hash algorithm.
The artificial intelligence image identification attack defense system is characterized in that the coding transmission decryption module decrypts the received coding information through a Hash algorithm.
The artificial intelligence image recognition attack defense system, wherein, the server end further comprises:
and the face identity data comparison module is used for acquiring an identity recognition result according to the real data information obtained through the background image target database.
The artificial intelligence image recognition attack defense system, wherein, the server end further comprises:
and the face characteristic picture searching module is used for acquiring a picture identification result according to the real data information obtained through the background image target database.
The artificial intelligence image recognition attack defense system, wherein, the server end further comprises:
and the image detection module is used for detecting the image again when the image target binary algorithm module judges that the image is attacked when the image is transmitted to the package.
The invention discloses an artificial intelligence image identification attack defense system, which comprises: the system comprises a monitor end and a server end, wherein the monitor end is in communication connection with the server end; the monitor end comprises an image target position detection module, an image digitization module, an image security coding module and a coding transmission encryption module, and the server end comprises a coding transmission decryption module, an image security decoding module, a numerical imaging module, an image target secondary classification algorithm module, an artificial intelligent deep learning server host and a background image target database; the invention carries out intelligent recognition of the human face through the artificial intelligent image recognition attack defense system, avoids the confusion attack of artificial intelligent image recognition, quickly judges the authenticity of the image target and improves the safety of human face recognition.
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FIG. 1 is a functional diagram of an embodiment of the system for defending against image recognition attacks.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the system for defending against image recognition attack of artificial intelligence according to the preferred embodiment of the present invention is an artificial intelligence image recognition attack defending system, wherein the system for defending against image recognition attack of artificial intelligence comprises:
the system comprises a monitor terminal 100 and a server terminal 200, wherein the monitor terminal 100 is in communication connection with the server terminal 200; the monitor terminal 100 includes: an image target position detection module 110, configured to perform target position identification on an image obtained by a monitor in real time, and extract a pixel point that needs security coding protection; an image digitizing module 120, configured to perform digital processing on the image; the image security coding module 130 is used for coding the digitized image by a security algorithm; the encoding transmission encryption module 140 is used for encrypting the encoding information and transmitting the encrypted encoding information to the server through network packets; the server 200 includes: a coding transmission decryption module 210, configured to decrypt the received coding information; the image security decoding module 220 is configured to decode the decrypted encoded information into a value before encoding of the original image, and restore the value to the original target image through a digital image algorithm; a numerical imaging module 230, configured to perform imaging processing on the numerical value; the image target classification algorithm module 240 is used for preliminarily judging the authenticity of the image target through a rule base artificially defined by the authenticity characteristics of the target; the artificial intelligence deep learning server host 250 is used for comparing and identifying the detailed characteristics of the image target and then transmitting the characteristic information to the background image target database; and the background image target database 260 is used for returning or displaying the real data information of the image target according to the application range after comparing the corresponding information of the image target characteristics.
The present invention first captures the current image in real time through the monitor hardware, then uses the image target position detection module 110 to identify the target (not all the pixels need to be security coded, only the key image part needs to be security coded and protected, therefore, the target identification needs to be performed first, and extracts the part needing security coded and protected, thereby reducing the traffic), and uses the image digitization module 120 to digitize the image, the digitized image enters the image security coding module 130, after the security algorithm coding, the coding information is encrypted by the coding transmission encryption module 140, and finally transmitted to the server 200 through the network package.
The image security coding module 130 may perform security coding at each pixel position of the image, transmit the picture coding information to the background (which is the background for processing the image and video) through the image digitizing module 120, and decode the picture coding information from the background into the original image; after the monitor hardware acquires the original image, the target identification is carried out on the content of the original image, and the added safety protection hardware carries out safety coding while the content of the target image is subjected to digital processing, so that an attacker is prevented from tampering the content of the image.
Further, the monitor terminal 100 further includes: a monitor for image capture; the monitor comprises a camera and a mobile terminal.
Wherein, the encoding transmission encryption module 140 encrypts the encoding information by a hash algorithm; the encoding transmission decryption module 210 decrypts the received encoded information through a hash algorithm.
Further, as shown in fig. 1, the server 200 further includes: a face identity data comparison module 270, configured to obtain an identity recognition result according to the real data information obtained through the background image target database; the face feature image searching module 280 is configured to obtain an image recognition result according to the real data information obtained through the background image target database.
Further, as shown in fig. 1, the server 200 further includes: the image detection module 290 is configured to detect the image again when the image target binary algorithm module 240 determines that the image has been attacked during packet transmission.
Specifically, the present invention first captures the current image in real time through the monitor hardware (or other image capturing devices), then identifies the target position by the image target position detection module 110, digitizes the image (for example, a human face) by the image digitization module 120, enters the image security coding module 130 (for example, a hash algorithm encryption module in this embodiment) through the digitized image, codes the security algorithm, then encrypts the coded information by the code transmission encryption module 140, and finally transmits the encrypted information to the server 200 through the network packet.
The server 200 decrypts the encoded information by using the encoding transmission decryption module 210, and decodes the decrypted encoded information into the original pre-encoded image value through the image security decoding module 220, and finally restores the original pre-encoded image value into the original target image through the digital imaging module 230.
Generally speaking, the artificial intelligence pattern recognition aliasing attack is carried out, (such as deep learning network differential evolution attack, fake false face recognition attack, etc., differential evolution is an optimization algorithm in artificial intelligence image recognition, the differential evolution attack is to change the pixels of the face picture transmitted to the server side from the local side, modify the single pixel point or multiple pixel points of the face picture, and make the judgment of the deep learning artificial intelligence (DNN) of the server side misaligned), the most probable attack point of a hacker is the pattern packet, and the process of transmitting the pattern packet to the host computer of the server side 200 through the network, namely the process of transmitting the pattern packet to the code transmission decryption module of the server side 200 from the code transmission encryption module 140 of the monitor side 100.
In order to avoid the risk of confusion of an artificial intelligent target identification server end caused by the confusion attack of artificial intelligent image identification, before a decrypted image enters an artificial intelligent deep learning server, the authenticity of the image target is preliminarily judged by a rule base artificially defined by target authenticity characteristics through an image target two-classification algorithm module 240 (because the operational capability of images such as a camera and video acquisition equipment is limited, an error exists in the identification of an image block similar to a human face, an image block which is not the human face but is similar to the human face is marked as a target image, and safety coding is carried out, in order to improve the efficiency of the next human face identification processing, a part which is not the human face needs to be firstly screened out, namely after the target image is found to be falsified through checking the safety coding, whether the falsified target is planned confusion attack or not is further analyzed, because in the image data transmission process, data transmission errors can also occur, not necessarily targeted attacks).
If the target does satisfy the target authenticity feature, the pattern allows further comparison via an artificial intelligence deep learning network (DNN). If not, it can be known that the picture may have been subjected to the hacking attack during the packet transmission, and the information cannot be directly inputted into the artificial intelligence recognition database to avoid database confusion, and the picture needs to be sent to the image detection module 290 for rechecking.
After the artificial intelligent deep learning network (DNN) further compares and identifies the detailed characteristics of the target, the characteristic information is transmitted to the processing center of the background target database 260, and after the corresponding information of the target characteristics is successfully compared by the background target database 260, the real data information of the target is returned or displayed according to the application range.
In summary, the present invention provides an artificial intelligence image recognition attack defense system, which includes: the system comprises a monitor end and a server end, wherein the monitor end is in communication connection with the server end; the monitor end comprises an image target position detection module, an image digitization module, an image security coding module and a coding transmission encryption module, and the server end comprises a coding transmission decryption module, an image security decoding module, a numerical imaging module, an image target secondary classification algorithm module, an artificial intelligent deep learning server host and a background image target database; the monitor end carries out target position identification on the image acquired in real time, acquires pixel points needing safety coding protection, and carries out digital processing on the image; the monitor end encodes the digitized image, encrypts encoded information and transmits the encrypted encoded information into the server end through a network packet; the server side decrypts the received coding information, restores the received coding information into an original target image, and preliminarily judges the authenticity of the image target after carrying out imaging processing on the numerical value; and the server end compares and identifies the detailed characteristics of the image target, compares the corresponding information of the image target characteristics, and returns or displays the real data information of the image target according to the application range. According to the invention, through intelligent recognition of the human face, the confusion attack of artificial intelligent pattern recognition is avoided, the authenticity of the image target is rapidly judged, and the safety of human face recognition is improved.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (5)
1. An artificial intelligence image recognition attack defense system, characterized in that the artificial intelligence image recognition attack defense system includes:
the system comprises a monitor end and a server end, wherein the monitor end is in communication connection with the server end;
the monitor side includes:
the image target position detection module is used for identifying the target position of the image acquired by the monitor in real time and extracting pixel points needing security coding protection;
the image digitization module is used for carrying out digitization processing on the image;
the image security coding module is used for coding the digitized image by a security algorithm;
the encoding transmission encryption module is used for encrypting the encoding information and transmitting the encrypted encoding information to the server end through network packets;
the server side includes:
the coding transmission decryption module is used for decrypting the received coding information;
the image security decoding module is used for decoding the decrypted coding information into a numerical value before the original image is coded and restoring the numerical value into an original target image through a digital image algorithm;
the numerical value imaging module is used for carrying out imaging processing on the numerical value;
the image target classification algorithm module is used for preliminarily judging the authenticity of the image target through a rule base artificially defined by the authenticity characteristics of the target; the risk that the artificial intelligent target identification server end is confused due to the confusion attack of the artificial intelligent image identification is avoided; the image target is a human face, and the image target is classified into authenticity of the human face;
the image detection module is used for detecting the image again when the image target binary algorithm module judges that the image is attacked when the image is transmitted to the package;
before the decrypted original target image enters the artificial intelligent deep learning server host, the decrypted original target image needs to enter an image target two-classification algorithm module;
the artificial intelligent deep learning server host is used for comparing and identifying the detailed characteristics of the image target and then transmitting the characteristic information to the background image target database;
the background image target database is used for returning or displaying the real data information of the image target according to the application range after comparing the corresponding information of the image target characteristics;
the face identity data comparison module is used for acquiring an identity recognition result according to the real data information obtained through the background image target database;
the face characteristic picture searching module is used for acquiring a picture identification result according to the real data information obtained through the background image target database;
through the intelligent recognition of the human face, the confusion attack of artificial intelligent pattern recognition is avoided, the authenticity of an image target is quickly judged, and the safety of the human face recognition is improved.
2. The system of claim 1, wherein the monitor terminal further comprises: a monitor for image capture.
3. The system of claim 2, wherein the monitor comprises a camera and a mobile terminal.
4. The system of claim 1, wherein the encoded transmission encryption module encrypts the encoded information by a hash algorithm.
5. The system of claim 1, wherein the encoded transmission decryption module decrypts the received encoded information by a hash algorithm.
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| CN109271137B (en) * | 2018-09-11 | 2020-06-02 | 网御安全技术（深圳）有限公司 | Modular multiplication device based on public key encryption algorithm and coprocessor |
| CN110070115B (en) * | 2019-04-04 | 2021-09-03 | 广州大学 | Single-pixel attack sample generation method, device, equipment and storage medium |
| CN110046622B (en) * | 2019-04-04 | 2021-09-03 | 广州大学 | Targeted attack sample generation method, device, equipment and storage medium |
| CN110263674B (en) * | 2019-05-31 | 2022-02-15 | 武汉大学 | Anti-reconnaissance camouflage 'invisible clothes' generation method for deep pedestrian re-identification system |
| CN111401273B (en) * | 2020-03-19 | 2022-04-29 | 支付宝(杭州)信息技术有限公司 | User feature extraction system and device for privacy protection |
| CN111274601A (en) * | 2020-03-23 | 2020-06-12 | 上海金桥信息股份有限公司 | Image security identification system and method under network environment |
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| CN105488486A (en) * | 2015-12-07 | 2016-04-13 | 清华大学 | Face recognition method and device for preventing photo attack |
| CN106778525A (en) * | 2016-11-25 | 2017-05-31 | 北京旷视科技有限公司 | Identity identifying method and device |
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| CN101957911A (en) * | 2010-09-29 | 2011-01-26 | 汉王科技股份有限公司 | Face identification method and system |
| CN105488486A (en) * | 2015-12-07 | 2016-04-13 | 清华大学 | Face recognition method and device for preventing photo attack |
| CN106778525A (en) * | 2016-11-25 | 2017-05-31 | 北京旷视科技有限公司 | Identity identifying method and device |
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