Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection
The study of LiDAR-based 3D object detection and its robustness under adversarial attacks has achieved great progress. However, existing adversarial attack methods mainly focus on the targeted object, which destroys the integrity of the object and makes the attack easy to perceive. In this work, we...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4409 |
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| author | Mumuxin Cai Xupeng Wang Ferdous Sohel Hang Lei |
| author_facet | Mumuxin Cai Xupeng Wang Ferdous Sohel Hang Lei |
| author_sort | Mumuxin Cai |
| collection | DOAJ |
| description | The study of LiDAR-based 3D object detection and its robustness under adversarial attacks has achieved great progress. However, existing adversarial attack methods mainly focus on the targeted object, which destroys the integrity of the object and makes the attack easy to perceive. In this work, we propose a novel adversarial attack against deep 3D object detection models named the contextual attribution maps-guided attack (CAMGA). Based on the combinations of subregions in the context area and their impact on the prediction results, contextual attribution maps can be generated. An attribution map exposes the influence of individual subregions in the context area on the detection results and narrows down the scope of the adversarial attack. Subsequently, perturbations are generated under the guidance of a dual loss, which is proposed to suppress the detection results and maintain visual imperception simultaneously. The experimental results proved that the CAMGA method achieved an attack success rate of over 68% on three large-scale datasets and 83% on the KITTI dataset. Meanwhile, the CAMGA has a transfer attack success rate of at least 50% against all four victim detectors, as they all overly rely on contextual information. |
| format | Article |
| id | doaj-art-9e7743f1dd40498892511afad06fd8b1 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9e7743f1dd40498892511afad06fd8b12025-08-20T02:50:36ZengMDPI AGRemote Sensing2072-42922024-11-011623440910.3390/rs16234409Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object DetectionMumuxin Cai0Xupeng Wang1Ferdous Sohel2Hang Lei3School of Information and Software Engineering, The University of Electronic Science and Technology of China, Chengdu 610054, ChinaLaboratory of Intelligent Collaborative Computing, The University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information Technology, Murdoch University, Murdoch, WA 6150, AustraliaSchool of Information and Software Engineering, The University of Electronic Science and Technology of China, Chengdu 610054, ChinaThe study of LiDAR-based 3D object detection and its robustness under adversarial attacks has achieved great progress. However, existing adversarial attack methods mainly focus on the targeted object, which destroys the integrity of the object and makes the attack easy to perceive. In this work, we propose a novel adversarial attack against deep 3D object detection models named the contextual attribution maps-guided attack (CAMGA). Based on the combinations of subregions in the context area and their impact on the prediction results, contextual attribution maps can be generated. An attribution map exposes the influence of individual subregions in the context area on the detection results and narrows down the scope of the adversarial attack. Subsequently, perturbations are generated under the guidance of a dual loss, which is proposed to suppress the detection results and maintain visual imperception simultaneously. The experimental results proved that the CAMGA method achieved an attack success rate of over 68% on three large-scale datasets and 83% on the KITTI dataset. Meanwhile, the CAMGA has a transfer attack success rate of at least 50% against all four victim detectors, as they all overly rely on contextual information.https://www.mdpi.com/2072-4292/16/23/4409adversarial attack3D object detectioncontextual informationtransferable attackattribution map |
| spellingShingle | Mumuxin Cai Xupeng Wang Ferdous Sohel Hang Lei Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection Remote Sensing adversarial attack 3D object detection contextual information transferable attack attribution map |
| title | Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection |
| title_full | Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection |
| title_fullStr | Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection |
| title_full_unstemmed | Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection |
| title_short | Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection |
| title_sort | contextual attribution maps guided transferable adversarial attack for 3d object detection |
| topic | adversarial attack 3D object detection contextual information transferable attack attribution map |
| url | https://www.mdpi.com/2072-4292/16/23/4409 |
| work_keys_str_mv | AT mumuxincai contextualattributionmapsguidedtransferableadversarialattackfor3dobjectdetection AT xupengwang contextualattributionmapsguidedtransferableadversarialattackfor3dobjectdetection AT ferdoussohel contextualattributionmapsguidedtransferableadversarialattackfor3dobjectdetection AT hanglei contextualattributionmapsguidedtransferableadversarialattackfor3dobjectdetection |