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Counter-reconnaissance camouflage 'invisible clothes' generation method for deep pedestrian re-identification system

A pedestrian re-identification and anti-reconnaissance technology, applied in character and pattern recognition, instruments, computing, etc., can solve security threats, spy intrusion and other problems

Active Publication Date: 2019-09-20
WUHAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Once the deep person re-identification system is vulnerable to specific attacks, it will bring serious consequences and security threats, for example, criminals can evade the search and location of law enforcement agencies, or spies can infiltrate classified classified areas under surveillance

Method used

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  • Counter-reconnaissance camouflage 'invisible clothes' generation method for deep pedestrian re-identification system
  • Counter-reconnaissance camouflage 'invisible clothes' generation method for deep pedestrian re-identification system
  • Counter-reconnaissance camouflage 'invisible clothes' generation method for deep pedestrian re-identification system

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Embodiment Construction

[0032] The present invention considers that the security issue of deep person re-identification has not been paid attention to, and it will bring potential security threats when it is widely used. Therefore, an anti-reconnaissance camouflage "invisibility cloak" generation method for deep person re-identification system is urgently needed.

[0033] The anti-reconnaissance camouflage "invisibility cloak" generation method designed by the present invention for the deep pedestrian re-identification system includes the following steps:

[0034] 1) Given a pedestrian re-identification system, the query image is input, and the system outputs the similarity and similarity ranking between images captured by other cameras and the query image. The attacker can access the parameters and weights of the target model, and set the specific users that the attacker wants to match.

[0035] The target person re-identification system can be expressed as f θ (x,y)=sc, where x is the image that t...

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Abstract

The invention discloses a counter-reconnaissance camouflage 'invisible clothes' generation method for a deep pedestrian re-identification system. A matching difference minimization optimization method is provided, and a cross-camera variable and position-expandable noise pattern is generated by combining multi-position sampling, so that the same noise pattern cannot be matched with each other under shooting of different cameras at any position of a pedestrian re-identification system monitoring area and is mistakenly matched with a specific user. In addition, according to the method, physical environment factors are fused into the noise pattern generation process, and the robustness of information loss of noise in the printing and shooting process is improved. The anti-reconnaissance camouflage 'invisible clothes' generated by the method can make the pedestrian re-identification system cannot correctly search and position attackers, and the attackers are matched into preset specific users by the cheating system.

Description

technical field [0001] The invention is designed in the field of artificial intelligence security, and specifically refers to a method for generating an anti-reconnaissance camouflage "invisibility cloak" oriented to a deep pedestrian re-identification system. Background technique [0002] With the rapid development of the mobile Internet, the continuous upgrading of hardware equipment, the production of massive data and the updating of algorithms, the development of artificial intelligence (AI) has become unstoppable, and it is gradually penetrating and profoundly changing human life. At present, artificial intelligence technology based on machine learning and deep learning is widely used in various fields such as human-computer interaction, visual processing system, recommendation system, safety diagnosis and protection, and its application scenarios include unmanned driving, image recognition, malware detection, Malware filtering and more. It can be said that the advent ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/23G06V10/751G06F18/241
Inventor 王志波郑思言宋梦凯王骞
Owner WUHAN UNIV
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