Fetching the paper…
Reading the bibliography…
Deep neural networks (DNNs) have been proven extremely susceptible to adversarial examples, which raises special safety-critical concerns for DNN-based autonomous driving stacks (i.e., 3D object detection).
J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,” ACM SIGGRAPH computer graphics , vol. 18, no. 3, 1984
1984
Earlier work this paper cites.
N. Max, “Optical models for direct volume rendering,” IEEE TVCG , 1995
1995
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” ICLR , 2014
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” ICLR , 2015
2015
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in ICLR Workshop , 2017
2017
Earlier work this paper cites.
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille, “Adversarial examples for semantic segmentation and object detection,” in CVPR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Kato, Y. Ushiku, and T. Harada, “Neural 3d mesh renderer,” in CVPR , 2018
2018
Earlier work this paper cites.
A. Majercik, C. Crassin, P. Shirley, and M. McGuire, “A ray-box intersection algorithm and efficient dynamic voxel rendering,” Journal of Computer Graphics Techniques Vol , vol. 7, no. 3, 2018
2018
Earlier work this paper cites.
C. Xiang, C. R. Qi, and B. Li, “Generating 3d adversarial point clouds,” in CVPR , 2019
2019
Earlier work this paper cites.
C. Xiao, D. Yang, B. Li, J. Deng, and M. Liu, “Meshadv: Adversarial meshes for visual recognition,” in CVPR , 2019
2019
Earlier work this paper cites.
X. Zeng, C. Liu, Y.-S. Wang, W. Qiu, L. Xie, Y.-W. Tai, C.-K. Tang, and A. L. Yuille, “Adversarial attacks beyond the image space,” in CVPR , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S.-T. Chen, C. Cornelius, J. Martin, and D. H. Chau, “Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector,” in ECML PKDD , 2019
2019
Earlier work this paper cites.
Y. Zhang, H. Foroosh, P. David, and B. Gong, “Camou: Learning physical vehicle camouflages to adversarially attack detectors in the wild,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
S. Liu, T. Li, W. Chen, and H. Li, “Soft rasterizer: A differentiable renderer for image-based 3d reasoning,” in ICCV , 2019
2019
Earlier work this paper cites.
Y. Dong, Q.-A. Fu, X. Yang, T. Pang, H. Su, Z. Xiao, and J. Zhu, “Benchmarking adversarial robustness on image classification,” in CVPR , 2020
2020
Earlier work this paper cites.
J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng, and R. Urtasun, “Physically realizable adversarial examples for lidar object detection,” in CVPR , 2020
2020
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV , 2020
2020
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR , 2020
2020
Cited alongside, same era.
K. Xu, G. Zhang, S. Liu, Q. Fan, M. Sun, H. Chen, P.-Y. Chen, Y. Wang, and X. Lin, “Adversarial t-shirt! evading person detectors in a physical world,” in ECCV , 2020
2020
Cited alongside, same era.
L. Huang, C. Gao, Y. Zhou, C. Xie, A. L. Yuille, C. Zou, and N. Liu, “Universal physical camouflage attacks on object detectors,” in CVPR , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
T. Bai, J. Luo, and J. Zhao, “Inconspicuous adversarial patches for fooling image-recognition systems on mobile devices,” IEEE Internet of Things Journal , vol. 9, no. 12, pp. 9515–9524, 2021
2021
Later among the works it cites.
2022
Later among the works it cites.
Y. Dong, S. Ruan, H. Su, C. Kang, X. Wei, and J. Zhu, “Viewfool: Evaluating the robustness of visual recognition to adversarial viewpoints,” NeurIPS , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
A. Hamdi, S. Rojas, A. Thabet, and B. Ghanem, “Advpc: Transferable adversarial perturbations on 3d point clouds,” in ECCV , 2020
2020
Cited alongside, same era.
A. Hamdi, M. Müller, and B. Ghanem, “Sada: semantic adversarial diagnostic attacks for autonomous applications,” in AAAI , 2020
2020
Cited alongside, same era.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in ECCV , 2020
2020
Cited alongside, same era.
T. Karras, S. Laine, Aittala, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” in CVPR , 2020
2020
Cited alongside, same era.
C. Xie, M. Tan, B. Gong, J. Wang, A. L. Yuille, and Q. V. Le, “Adversarial examples improve image recognition,” in CVPR , 2020
2020
Cited alongside, same era.
H. Mohaghegh Dolatabadi, S. Erfani, and C. Leckie, “Advflow: Inconspicuous black-box adversarial attacks using normalizing flows,” NeurIPS , 2020
2020
Cited alongside, same era.
Y. Cao, N. Wang, C. Xiao, D. Yang, J. Fang, R. Yang, Q. A. Chen, M. Liu, and B. Li, “Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks,” in IEEE Symposium on Security and Privacy (SP) , 2021
2021
Cited alongside, same era.
Y. Ma, T. Wang, X. Bai, H. Yang, Y. Hou, Y. Wang, Y. Qiao, R. Yang, D. Manocha, and X. Zhu, “Vision-centric bev perception: A survey,” 2022
2022
Later among the works it cites.
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in CVPR , 2022
2022
Later among the works it cites.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM ToG , 2022
2022
Later among the works it cites.
J. Gu, L. Liu, P. Wang, and C. Theobalt, “Stylenerf: A style-based 3d aware generator for high-resolution image synthesis,” in ICLR , 2022
2022
Later among the works it cites.
E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. D. Mello, O. Gallo, L. Guibas, J. Tremblay, S. Khamis, T. Karras, and G. Wetzstein, “Efficient geometry-aware 3D generative adversarial networks,” in CVPR , 2022
2022
Later among the works it cites.
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” ECCV , 2022
2022
Later among the works it cites.
S. Jia, B. Yin, T. Yao, S. Ding, C. Shen, X. Yang, and C. Ma, “Adv-attribute: Inconspicuous and transferable adversarial attack on face recognition,” NeurIPS , 2022
2022
Later among the works it cites.
Z. Wang, W. Chen, D. Acuna, J. Kautz, and S. Fidler, “Neural light field estimation for street scenes with differentiable virtual object insertion,” in ECCV , 2022
2022
Later among the works it cites.
L. Li, Q. Lian, L. Wang, N. Ma, and Y.-C. Chen, “Lift3d: Synthesize 3d training data by lifting 2d gan to 3d generative radiance field,” in CVPR , 2023
2023
Closest in time.
2023
Closest in time.
S. Xie, Z. Li, Z. Wang, and C. Xie, “On the adversarial robustness of camera-based 3d object detection,” TMLR , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Chen, Z. Chen, G. P. Meyer, D. Park, C. Vondrick, A. Shrivastava, and Y. Chai, “Shift3d: Synthesizing hard inputs for tricking 3d detectors,” in ICCV , 2023
2023
Closest in time.
Z. Yang, Y. Chen, J. Wang, S. Manivasagam, W.-C. Ma, A. J. Yang, and R. Urtasun, “Unisim: A neural closed-loop sensor simulator,” in CVPR , 2023
2023
Closest in time.
Y. Li, Z.-H. Lin, D. Forsyth, J.-B. Huang, and S. Wang, “Climatenerf: Extreme weather synthesis in neural radiance field,” in CVPR , 2023
2023
Closest in time.