3D adversarial attacks beyond point cloud
Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Jinlai [verfasserIn] Chen, Lyujie [verfasserIn] Liu, Binbin [verfasserIn] Ouyang, Bo [verfasserIn] Xie, Qizhi [verfasserIn] Zhu, Jihong [verfasserIn] Li, Weiming [verfasserIn] Meng, Yanmei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Information sciences - New York, NY : Elsevier Science Inc., 1968, 633, Seite 491-503 |
|---|---|
| Übergeordnetes Werk: |
volume:633 ; pages:491-503 |
| DOI / URN: |
10.1016/j.ins.2023.03.084 |
|---|
| Katalog-ID: |
ELV009492488 |
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| 245 | 1 | 0 | |a 3D adversarial attacks beyond point cloud |
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| 520 | |a Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. | ||
| 650 | 4 | |a Adversarial attack | |
| 650 | 4 | |a Point cloud classification | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Mesh | |
| 700 | 1 | |a Chen, Lyujie |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Binbin |e verfasserin |4 aut | |
| 700 | 1 | |a Ouyang, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Qizhi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Jihong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Weiming |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Yanmei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Information sciences |d New York, NY : Elsevier Science Inc., 1968 |g 633, Seite 491-503 |h Online-Ressource |w (DE-627)271175850 |w (DE-600)1478990-5 |w (DE-576)078412293 |x 0020-0255 |7 nnns |
| 773 | 1 | 8 | |g volume:633 |g pages:491-503 |
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10.1016/j.ins.2023.03.084 doi (DE-627)ELV009492488 (ELSEVIER)S0020-0255(23)00391-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Zhang, Jinlai verfasserin aut 3D adversarial attacks beyond point cloud 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. Adversarial attack Point cloud classification Deep learning Mesh Chen, Lyujie verfasserin aut Liu, Binbin verfasserin aut Ouyang, Bo verfasserin aut Xie, Qizhi verfasserin aut Zhu, Jihong verfasserin aut Li, Weiming verfasserin aut Meng, Yanmei verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 633, Seite 491-503 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:633 pages:491-503 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 633 491-503 |
| spelling |
10.1016/j.ins.2023.03.084 doi (DE-627)ELV009492488 (ELSEVIER)S0020-0255(23)00391-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Zhang, Jinlai verfasserin aut 3D adversarial attacks beyond point cloud 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. Adversarial attack Point cloud classification Deep learning Mesh Chen, Lyujie verfasserin aut Liu, Binbin verfasserin aut Ouyang, Bo verfasserin aut Xie, Qizhi verfasserin aut Zhu, Jihong verfasserin aut Li, Weiming verfasserin aut Meng, Yanmei verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 633, Seite 491-503 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:633 pages:491-503 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 633 491-503 |
| allfields_unstemmed |
10.1016/j.ins.2023.03.084 doi (DE-627)ELV009492488 (ELSEVIER)S0020-0255(23)00391-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Zhang, Jinlai verfasserin aut 3D adversarial attacks beyond point cloud 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. Adversarial attack Point cloud classification Deep learning Mesh Chen, Lyujie verfasserin aut Liu, Binbin verfasserin aut Ouyang, Bo verfasserin aut Xie, Qizhi verfasserin aut Zhu, Jihong verfasserin aut Li, Weiming verfasserin aut Meng, Yanmei verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 633, Seite 491-503 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:633 pages:491-503 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 633 491-503 |
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10.1016/j.ins.2023.03.084 doi (DE-627)ELV009492488 (ELSEVIER)S0020-0255(23)00391-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Zhang, Jinlai verfasserin aut 3D adversarial attacks beyond point cloud 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. Adversarial attack Point cloud classification Deep learning Mesh Chen, Lyujie verfasserin aut Liu, Binbin verfasserin aut Ouyang, Bo verfasserin aut Xie, Qizhi verfasserin aut Zhu, Jihong verfasserin aut Li, Weiming verfasserin aut Meng, Yanmei verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 633, Seite 491-503 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:633 pages:491-503 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 633 491-503 |
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10.1016/j.ins.2023.03.084 doi (DE-627)ELV009492488 (ELSEVIER)S0020-0255(23)00391-2 DE-627 ger DE-627 rda eng 070 004 VZ LING DE-30 fid 54.00 bkl 53.71 bkl Zhang, Jinlai verfasserin aut 3D adversarial attacks beyond point cloud 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. Adversarial attack Point cloud classification Deep learning Mesh Chen, Lyujie verfasserin aut Liu, Binbin verfasserin aut Ouyang, Bo verfasserin aut Xie, Qizhi verfasserin aut Zhu, Jihong verfasserin aut Li, Weiming verfasserin aut Meng, Yanmei verfasserin aut Enthalten in Information sciences New York, NY : Elsevier Science Inc., 1968 633, Seite 491-503 Online-Ressource (DE-627)271175850 (DE-600)1478990-5 (DE-576)078412293 0020-0255 nnns volume:633 pages:491-503 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ 53.71 Theoretische Nachrichtentechnik VZ AR 633 491-503 |
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Zhang, Jinlai @@aut@@ Chen, Lyujie @@aut@@ Liu, Binbin @@aut@@ Ouyang, Bo @@aut@@ Xie, Qizhi @@aut@@ Zhu, Jihong @@aut@@ Li, Weiming @@aut@@ Meng, Yanmei @@aut@@ |
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2023-01-01T00:00:00Z |
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3d adversarial attacks beyond point cloud |
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3D adversarial attacks beyond point cloud |
| abstract |
Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. |
| abstractGer |
Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. |
| abstract_unstemmed |
Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud needs to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code will be available at: https://github.com/cuge1995/Mesh-Attack. |
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3D adversarial attacks beyond point cloud |
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Chen, Lyujie Liu, Binbin Ouyang, Bo Xie, Qizhi Zhu, Jihong Li, Weiming Meng, Yanmei |
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| score |
7.399638 |