Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer
Adversarial attacks that mislead deep neural networks (DNNs) into making incorrect predictions can also be implemented in the physical world. However, most of the existing adversarial camouflage textures that attack object detection models only consider the effectiveness of the attack, ignoring the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/26/11/903 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145206551183360 |
|---|---|
| author | Wei Cai Xingyu Di Xin Wang Weijie Gao Haoran Jia |
| author_facet | Wei Cai Xingyu Di Xin Wang Weijie Gao Haoran Jia |
| author_sort | Wei Cai |
| collection | DOAJ |
| description | Adversarial attacks that mislead deep neural networks (DNNs) into making incorrect predictions can also be implemented in the physical world. However, most of the existing adversarial camouflage textures that attack object detection models only consider the effectiveness of the attack, ignoring the stealthiness of adversarial attacks, resulting in the generated adversarial camouflage textures appearing abrupt to human observers. To address this issue, we propose a style transfer module added to an adversarial texture generation framework. By calculating the style loss between the texture and the specified style image, the adversarial texture generated by the model is guided to have good stealthiness and is not easily detected by DNNs and human observers in specific scenes. Experiments have shown that in both the digital and physical worlds, the vehicle full coverage adversarial camouflage texture we create has good stealthiness and can effectively fool advanced DNN object detectors while evading human observers in specific scenes. |
| format | Article |
| id | doaj-art-a33bf8f092c84dfbab9585d593211280 |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-a33bf8f092c84dfbab9585d5932112802025-08-20T02:28:09ZengMDPI AGEntropy1099-43002024-10-01261190310.3390/e26110903Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style TransferWei Cai0Xingyu Di1Xin Wang2Weijie Gao3Haoran Jia4The Third Faculty of Xi’an Research Institute of High Technology, Xi’an 710064, ChinaThe Third Faculty of Xi’an Research Institute of High Technology, Xi’an 710064, ChinaThe Third Faculty of Xi’an Research Institute of High Technology, Xi’an 710064, ChinaThe Third Faculty of Xi’an Research Institute of High Technology, Xi’an 710064, ChinaThe Third Faculty of Xi’an Research Institute of High Technology, Xi’an 710064, ChinaAdversarial attacks that mislead deep neural networks (DNNs) into making incorrect predictions can also be implemented in the physical world. However, most of the existing adversarial camouflage textures that attack object detection models only consider the effectiveness of the attack, ignoring the stealthiness of adversarial attacks, resulting in the generated adversarial camouflage textures appearing abrupt to human observers. To address this issue, we propose a style transfer module added to an adversarial texture generation framework. By calculating the style loss between the texture and the specified style image, the adversarial texture generated by the model is guided to have good stealthiness and is not easily detected by DNNs and human observers in specific scenes. Experiments have shown that in both the digital and physical worlds, the vehicle full coverage adversarial camouflage texture we create has good stealthiness and can effectively fool advanced DNN object detectors while evading human observers in specific scenes.https://www.mdpi.com/1099-4300/26/11/903physical attackneural style transferstealthy adversarial attackwhite-box attackobject detection |
| spellingShingle | Wei Cai Xingyu Di Xin Wang Weijie Gao Haoran Jia Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer Entropy physical attack neural style transfer stealthy adversarial attack white-box attack object detection |
| title | Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer |
| title_full | Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer |
| title_fullStr | Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer |
| title_full_unstemmed | Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer |
| title_short | Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer |
| title_sort | stealthy vehicle adversarial camouflage texture generation based on neural style transfer |
| topic | physical attack neural style transfer stealthy adversarial attack white-box attack object detection |
| url | https://www.mdpi.com/1099-4300/26/11/903 |
| work_keys_str_mv | AT weicai stealthyvehicleadversarialcamouflagetexturegenerationbasedonneuralstyletransfer AT xingyudi stealthyvehicleadversarialcamouflagetexturegenerationbasedonneuralstyletransfer AT xinwang stealthyvehicleadversarialcamouflagetexturegenerationbasedonneuralstyletransfer AT weijiegao stealthyvehicleadversarialcamouflagetexturegenerationbasedonneuralstyletransfer AT haoranjia stealthyvehicleadversarialcamouflagetexturegenerationbasedonneuralstyletransfer |